Logistic & Service Optimization for O&M of Offshore Wind Farms

Logistic & Service Optimization for O&M of Offshore Wind Farms Model Development & Output Analysis

Ashish Dewan Master of Science Thesis

Faculty of Aerospace Engineering

Logistic & Service Optimization for O&M of Oﬀshore Wind Farms Model Development & Output Analysis

Master of Science Thesis

For the degree of Master of Science in Sustainable Energy Technology at Delft University of Technology

Ashish Dewan

March 25, 2014

Faculty of Applied Sciences (TNW) · Delft University of Technology The work in this thesis was supported by Fraunhofer Institute for Wind Energy and Energy System Technology (IWES) . Their cooperation is hereby gratefully acknowledged.

The undersigned hereby certify that they have read and recommend to the Faculty of Applied Sciences (TNW) for acceptance a thesis entitled Logistic & Service Optimization for O&M of Offshore Wind Farms by Ashish Dewan in partial fulﬁllment of the requirements for the degree of Master of Science Sustainable Energy Technology

Dated: March 25, 2014

Supervisor(s): Prof.dr. G.J.W.van Bussel

Dipl.-Ing.,M.Sc. K. Raﬁk

Reader(s): Dr.ir. W.A.A.M. Bierbooms

Dr. H. Peng

Abstract

The offshore wind industry is growing fast with an average annual market growth rate of almost 7% in the next five years. Service of wind turbines has proven to be expensive and difficult, especially offshore. A well-coordinated support organisation, optimized logistic and maintenance strategies are required to effectively reduce the costs associated with wind farm support. The thesis work focuses on developing a stochastic time based Logistic and Service model to analyse wind farm maintenance and logistic support organisation. The primary objective is to obtain the most cost-effective strategy, besides improving the availability of the wind farm. Within the scope of project, the wind turbine technology and the causes of its failures are reviewed. From the sub-assembly components, a list of critical spare parts is short-listed. As part of the data analysis and field studies, reliability patterns of the wind turbines are obtained from the Fraunhofer IWES WMEP database. The spare part support organisation is based on two running wind farms, namely Nysted and OWEZ. The weather information provided by the FINO 1 and FINO 2 MET masts are employed to estimate the accessibility to the wind farm. Further, based on the classification of the maintenance type of spare parts, the logistic and service strategies are implemented. The optimization of logistic and service model has been done separately, where the best possible values of ordering parameters and service aspects like finding the right access vessel strategy, the crew strength, the shift patterns, the feasibility of an offshore accommodation or renting of huge ships like mother vessel are explored. To verify the working of the model, sensitivity analysis, comparison studies and extreme value testing are performed. The model is able to predict a suitable strategy for a given wind farm, which is shown by implementing the model for a planned wind farm. With the developed O&M model, accurate inventory stocks, downtime, availability, service and logistic parameters and hence the inventory and maintenance cost is obtained. The results from the reference farms are encouraging as different strategies are compared for a cost-effective solution.

Master of Science Thesis Ashish Dewan ii

Ashish Dewan Master of Science Thesis Table of Contents

Acknowledgments ix

Acronyms xi

Deﬁnitions xiii

1 Introduction1 1-1 Offshore Wind Energy...... 2 1-2 Offshore Operation and Maintenance...... 2 1-3 Fraunhofer MAS Project...... 3 1-4 Economic Understanding...... 4 1-5 Research Objectives...... 4 1-5-1 Research Aim...... 4 1-5-2 Research Questions...... 4 1-6 Approach & Methodology...... 5 1-6-1 Research Approach...... 5 1-6-2 Literature Studies...... 5 1-6-3 Field Studies & Data Processing...... 5 1-6-4 Theoretical Logistic & Service Model...... 6 1-6-5 MATLAB Modelling...... 6 1-6-6 Verification & Analysis...... 6 1-7 Outline of the Thesis Report...... 6

2 Wind Turbine Technology7 2-1 Wind Energy Industry...... 7 2-1-1 Wind Farms...... 7 2-1-2 Wind Turbine Sub assemblies- Functionality & Failure Characteristics.. 8 2-2 Cost Significant items within a wind turbine...... 15 2-3 The Effect of Logistic & Service efficiency on WT availability...... 15

Master of Science Thesis Ashish Dewan iv Table of Contents

3 Theoretical Background 19 3-1 Maintenance Activities...... 20 3-2 Multi-Echelon system optimization...... 21 3-3 Spare Part Demand...... 21 3-4 Diﬀerent Ordering Policies...... 22 3-5 Single-Item vs. Multi-Item Inventory Optimization...... 23 3-6 MTTR Modelling and Estimation...... 24 3-7 Economic Parameters...... 24 3-8 Logistic and Service Models: Existing Models & Theory...... 25

4 Logistic Model 29 4-1 Global Assumptions of the O&M model...... 29 4-2 O&M Model Framework...... 30 4-3 Logistic Model Scope, Delimitations & Characteristics...... 32 4-3-1 Scope of the Logistic Model...... 32 4-3-2 Delimitations of the Logistic Model...... 32 4-3-3 Characteristics of the Logistic Model...... 33 4-4 Logistic Model Overview...... 33 4-5 Logistic Inputs...... 33 4-6 Single-Item Logistic Evaluation...... 34 4-6-1 Operation: Spare Part Demand Estimation...... 34 4-6-2 Operation: Stock Handling...... 35 4-6-3 Operation: Inventory Parameter Computation...... 36 4-6-4 Operation: Inventory Cost...... 36 4-7 Single-Item Logistic Optimization...... 36

5 Service Model 39 5-1 Classes for type of Maintenance...... 39 5-2 Service Model Scope & Assumptions...... 40 5-2-1 Scope of service aspects...... 40 5-2-2 Assumptions of the service model...... 41 5-3 Service Model Overview...... 41 5-4 Service Inputs...... 42 5-5 Service Simulation Block...... 44 5-5-1 Operation: Weather Time Series...... 44 5-5-2 Operation: Accessibility Vector...... 44 5-5-3 Operation: Corrective Replacement...... 44 5-5-4 Operation: Mean Time to Repair Modelling...... 44 5-5-5 Operation: Mean Logistic Delay Time & Access Delay Estimation.... 46 5-5-6 Operation: Mean Waiting Time Evaluation...... 47 5-5-7 Operation: Scheduled Maintenance...... 48 5-6 Service Outputs...... 49

Ashish Dewan Master of Science Thesis Table of Contents v

6 Field Studies and Data Processing 51 6-1 Spare Part Data...... 51 6-1-1 Spare Part Selection...... 51 6-1-2 Spare Part Price...... 52 6-1-3 Spare Part Type...... 52 6-1-4 Spare Part lead Times...... 52 6-2 Wind Turbine Failure and Downtime Data...... 53 6-2-1 Failure Data Processing...... 53 6-2-2 Mean Time to Repair (MTTR) Data Processing...... 54 6-3 Weather Time series Preparation...... 55 6-3-1 Weather Time Series Processing...... 55 6-3-2 Accessibility of Access Vessels...... 56 6-4 Logistics and Services Data...... 56 6-4-1 Reference wind farms and Base Scenario Characterization...... 57 6-5 Economic Data Considerations...... 60

7 Model Veriﬁcation and Analysis 61 7-1 Sensitivity Analysis...... 61 7-1-1 Logistic Model: Sensitivity Analysis for Lead time to Local or Central Ware- house...... 63 7-1-2 Service Model: Sensitivity Analysis for selection of Access Vessels.... 63 7-1-3 Service Model: Sensitivity Analysis for Lead time of Contract-based Vessels 64 7-1-4 Service Model: Sensitivity Analysis for Crew Strategy Working patterns. 65 7-2 Comparison Study of Logistic & Service Strategies...... 67 7-2-1 Logistic Model: (R,Q) policy vs. (S − 1,S) policy for Central Workshop 67 7-2-2 Service Model: Oﬀshore Accommodation Possibility...... 69 7-2-3 Service Model: Crew Shift Working Strategies...... 70 7-2-4 Service Model: Access Vessel Strategies performing Schedule Maintenance 72 7-3 Extreme Value Testing...... 81 7-3-1 Service Model: Distance from shore for Access Vessel Operation..... 81 7-3-2 Service Model: Maximum Crew allowed on a Vessel...... 81 7-4 Implementation of Model for Planned Wind Farm...... 81

8 Conclusions 83

A Logistic & Access Vessel Delay Decision Flow 85

B GUI Implementation 87 B-1 Features of GUI:...... 87

Master of Science Thesis Ashish Dewan vi Table of Contents

C Spare Part Data 89 C-1 Maintenance & Repair Report (WMEP)...... 89 C-2 Spare Part Data...... 90 C-3 Failure Rate/Demand Rate for Corrective Replacements...... 91

D MTTR Data Processing 93 D-1 Estimation of MTTR...... 93 D-2 MTTR Values as Inputs...... 95

E Support Organisation Information 97 E-1 Reference Wind Farms (Depot Locations)...... 97 E-2 Reference Wind Farms (Central Workshop to Depot Distance Estimates).... 98 E-3 Access Vessels employed for Maintenance...... 99

F Weather Time Series Preparation 101 F-1 Met Mast Data Sheet...... 101 F-2 Weather Time Series Processing...... 101

G Class of spares-Type of Maintenance 103

H Event List 107

Bibliography 109

Ashish Dewan Master of Science Thesis List of Figures

1-1 Fraunhofer IWES MAS Module [1]...... 3 1-2 The working process...... 5

2-1 Modern WT with Sub assembly description (adapted from [2])...... 8 2-2 Causes of Oﬀshore Wind turbine failure in the Netherlands [3]...... 9 2-3 Relative cost for the main components of an oﬀshore wind turbine [4]...... 15

3-1 Typical Layout of an Oﬀshore wind farm with Support Organization [5]..... 19 3-2 A typical layout of a Multi-Echelon System...... 21 3-3 Universal Bathtub Curve...... 22 3-4 Lognormal Distribution for estimating MTTR...... 25

4-1 Model Framework implementing Logistics & Services for an offshore wind farm. 31 4-2 Simplified flowchart for the Logistic Model...... 34 4-3 Flowchart of the Logistic Evaluation Block...... 35

5-1 Simpliﬁed ﬂowchart of the Service Model...... 42 5-2 Flowchart of the Service Operation Block...... 45

6-1 Location for three FINO Met Masts [www.ﬁno-oﬀshore.de/de/]...... 55

7-1 Sensitivity Analysis for change in Lead Time of the spare part at depot..... 63 7-2 Sensitivity Analysis for change in Access Vessel Speed at (i) 50 km and (ii) 100 km distance from shore...... 64 7-3 Sensitivity Analysis for change in Wind Speed Threshold...... 64 7-4 Sensitivity Analysis for change in Lead Time of a Contract Vessel...... 65 7-5 Sensitivity Analysis for change in Shift Working Hours...... 66

Master of Science Thesis Ashish Dewan viii List of Figures

7-6 Sensitivity Analysis for change in Minimum feasible Hours...... 66 7-7 Inventory Cost following a (S − 1,S) policy with two separate Central Workshops 68 7-8 Inventory Cost following a (R,Q) policy with a single Central Workshop..... 68 7-9 Oﬀshore Accommodation installed at Horns Rev-2...... 69 7-10 Mother Vessel Sketch [6]...... 73 7-11 Sensitivity with Distance...... 78 7-12 Sensitivity Veriﬁcation with the month of Scheduled Maintenance...... 78 7-13 Sensitivity with Distance and Number of turbines (Case1)...... 79 7-14 Sensitivity with Distance and Number of turbines (Case4)...... 80 7-15 Gradient Analysis: Case 1 [L] and Case 4 [R]...... 80 7-16 Comparison of Strategies for three planned wind farms...... 82

A-1 Flowchart for estimating the MLDT and Access Vessel Delay...... 86

B-1 GUI for Logistic & Service Model...... 87

D-1 Sample Lognormal distribution of MTTR...... 94

E-1 Egmond aan Zee (OWEZ) Wind Farm operated from the depot Ijmuiden (near Wijk aan Zee), Netherlands [7]...... 97 E-2 Nysted (Rødsand) Wind Farm operated from the depot Port of Rodby/ Gedser (small ferry harbor), Denmark [7]...... 98 E-3 Estimated distance between the Main Central Workshop of Vestas (Randers), Den- mark and the local depot (IJmuiden), Netherlands- for OWEZ wind farm (Google Maps)...... 98 E-4 Estimated distance between the Main Central Workshop of Siemens (Brande) and the local depot (Port of Gedser), Denmark - for Nysted Oﬀshore wind farm (Google Maps)...... 98 E-5 FOB Lady for Class B/Class E repair...... 99 E-6 Crane Vessel for Class C repair...... 100 E-7 Jack-up barge for Class D repair...... 100

F-1 Wind Time Series before processing...... 102 F-2 Wind Time Series after processing...... 102

Ashish Dewan Master of Science Thesis List of Tables

2-1 Wind Turbine Sub-assembly...... 14

4-1 Inputs to the Logistic Model...... 33

5-1 Service Model Inputs...... 43 5-2 Outputs generated from the Service Model...... 49

6-1 Accessibility of Access Vessels w.r.t two Met Masts...... 56 6-2 Wind Farm Description: Reference Wind Farms...... 57

7-1 Input Parameters for Validation of the model...... 62 7-2 Comparison with or without an offshore accommodation based on the lifetime analysis of a WF...... 70 7-3 Difference in Net Revenue for a WF with and without offshore accommodation. 71 7-4 Comparison of three different Crew Strategies...... 72 7-5 Comparison for choosing overnight in large contract based vessels...... 73 7-6 Summary of input parameters for the comparison of access strategy...... 75 7-7 Simulation Inputs for the arbitrary wind farm (sensitivity analysis)...... 77 7-8 Extreme value testing for distance from shore ...... 81 7-9 Extreme value testing for Crew Strength ...... 81 7-10 Input Parameters for planned WFs...... 82

D-1 Raw MTTR values (in minutes)...... 93 D-2 Frequency of MTTR Values...... 94

F-1 Characteristics of MET masts...... 101

H-1 Event List generated as an output from Service Model...... 108

Master of Science Thesis Ashish Dewan x List of Tables

Ashish Dewan Master of Science Thesis Acknowledgments

The completion of the Master thesis would not have been possible without the able assistance of some people, whom I would like to thank. Firstly, I would like to thank my University supervisor Prof. dr. Gerard J.W. van Bussel.I consider myself fortunate to have you as my supervisor. Without your attention, enthusiasm and guidance, my work would not have been productive. Your approach towards problem solving and defining the project scope helped me to streamline the work and the report itself. Further, I am grateful to Fraunhofer IWES for providing me an opportunity to experience their research institute. Their cooperation and assistance during the project period is truly appreciable. But sincerely, I would like to thank my daily supervisor Mr. ir. Khalid Rafik, who was always available to help me out during the 6 months period of mine at IWES. I honestly appreciate your willingness to discuss even the smallest of problems with me during the course of this work. Moreover, a special thanks to all my colleagues at Fraunhofer for being generous enough to a non- German speaker. Also, I am grateful to Dr. Engin Topan and Dr. Hao Peng from TU Eindhoven for their participation and out of the way help for making me understand and implement inventory theory in offshore wind energy scenario. I want to express my gratitude to the rest of the examination committee. I appreciate their time devoted to reading and evaluating this document. My special gratitude to my fellow friends at TU Delft- Akshay Hattiangadi, Maneesh Kumar Verma, Nishant Narayan, Prakhar Kapoor and Sidharth Mahalingam for their enthusiasm and help during the testing periods of the project. Finally, I would like to thank my family for their selfless love and support in completion of the studies and the project in particular.

Delft, University of Technology Ashish Dewan March 25, 2014

Master of Science Thesis Ashish Dewan xii Acknowledgments

Ashish Dewan Master of Science Thesis Acronyms

O&M Operation and Maintenance MLDT Mean Logistic Delay Time MTTR Mean Time To Repair MWT Mean Waiting Time WF Wind Farm OWEZ Oﬀshore Wind Farm Egmond aan Zee FINO Forschungsplattformen in Nord- und Ostsee WT Wind Turbine GUI Graphical User Interface WMEP Scientiﬁc Monitoring and Evaluation Program LCC Life Cycle Cost LSC Life Support Cost OPEX Operational Expenditure CAP EX Capital Expenditure MAS Multi Agent System MTBF Mean Time Between Failures MDT Mean Downtime MTBM Mean Time Between Maintenance LOP Loss of Production CBM Condition Based Maintenance CW Central workshop METRIC Multi Echelon Technique for Recoverable Items SCADA Supervisory Control and Data Acquisition

Master of Science Thesis Ashish Dewan xiv Acronyms

Ashish Dewan Master of Science Thesis Deﬁnitions

• Spare part is a replaceable unit for a technical system, e.g. a wind power system. Spare parts are used to repair a WT when any of its spare fails [8].

• Spare part stock, or only stock, is the spare parts stored for future use. The facility where this is done is referred to as a depot (a warehouse). A depot can be located right next to a WT site or some distance away. For oﬀshore wind farms, it is normally at the nearest harbour [8].

• Spare part strategy is the result of a number of decision variables regarding initial spare part investment, reorder points and allocation between depots [8].

• Support organisation is the complete arrangement that maintains a technical system, such as a wind farm, and provides it with personnel and equipment made available all the time. A support organisation has a certain structure, where depots, service stations and workshops, in some way are connected to each other [8].

• Reliability of a sub-assembly is the probability that it will perform its required function under stated conditions for a speciﬁed period of time. The unreliability is related to the failure intensity function, λ(t) [9].

• Availability is a fundamental measure of reliability. It combines both the outage time when an interruption has occurred and the frequency of interruption [10].

• Failure is the inability of a sub-assembly to perform its required function under deﬁned conditions; the item is then in a failed state, in contrast to an operational or working state [11].

Master of Science Thesis Ashish Dewan xvi Deﬁnitions

Ashish Dewan Master of Science Thesis Chapter 1

Introduction

The world needs a transition from its current unsustainable energy paradigm to a future powered entirely by renewable energy supply. The development of wind power has been steady in recent years, speciﬁcally in Europe, North America and developing countries like India and China. By end of the year 2012, the worldwide capacity by wind power generation reached 273 GW [12].

The current trend of the wind energy industry is to expand by developing more wind farms using turbines of high capacity ratings. Globally, very significant financial investments have been made in developing wind farms with a wide range of stakeholders. However, with this huge investment potential and significant increase in generation capacity comes an additional and often overlooked responsibility- the management of wind farms to ensure the lowest total Life Cycle Cost (LCC).

Proﬁt from a wind farm is the revenue generated by sale of electricity minus the investment cost and the operation and maintenance (O&M) expenditure [10], the latter being the focus of this thesis research. Thus, to increase the productivity and proﬁtability of the existing wind farms and to ensure the lowest total LCC for successful future developments, will require maintenance strategies that are appropriate (technically feasible and economically viable) over the life-cycle of wind turbines.

A well-coordinated support organization and optimized maintenance strategies are required to eﬀectively reduce the costs associated with the WT support. This also includes handling and storage of spare parts, commissioning of access vessels at the right time, employing the appropriate transport and crew strategy, etc. These types of problems are common in most of the industries and so is applicable for WT industry as well. If these processes are re-optimized, there is substantial money to be saved over time. These circumstances have built the ground for this thesis research.

Master of Science Thesis Ashish Dewan 2 Introduction

1-1 Oﬀshore Wind Energy

Large scale wind power has recently grown offshore due to lack of space in densely populated areas, aesthetics and noise issues, social acceptance, as well as favourable wind resources. An offshore wind farm typically consists of large multi-megawatt wind turbines clustered together in an offshore location some kilometres far from the coast, feeding the high voltage grid on an onshore connection point through cables that are carefully buried in the seabed. Since early nineties these kinds of projects have been realized mainly in Denmark, UK and the Netherlands. These countries possess advanced wind energy know-how, offshore experience from oil and gas platforms, and favourable characteristics such as shallow sea-water, strong winds and reliable electrical grids. Nowadays offshore wind farms are gaining increasing interest as an alternative option for electricity generation and various projects are under development (in the Netherlands, Belgium, UK, Denmark, Germany, Spain, and USA) [13]. At the moment, 3.8 GW is the installed capacity from the offshore wind farms in European countries. However, more than 40 GW is expected to be in operation by 2020 [14]. The development of offshore sector is essential for the majority of the EU countries to meet their respective targets of renewable energy production. The offshore wind energy industry, which is globally at a nascent stage has to face lots of challenges and find solutions mainly because of special conditions encountered in the marine environment. Extra loads due to waves and currents, water depths, and soil properties of the seabed are just some of the other factors that have to be considered during the structural design of an offshore wind turbine. Moreover, the saline environment accelerates unfavourable processes such as corrosion and crack growth in the structure. For this, the offshore wind turbines require coatings and materials, which are usually more expensive than the ones used onshore. Additionally, some other vital technical and economic issues in which offshore wind energy differs from onshore is its technology, availability, energy production, capital cost, operation and maintenance activities, etc. [15]. When initially developed, offshore wind farms were not much different from onshore, since the same wind turbine technology with slight modifications was being directly applied to the new environment. In early wind farms, low water depths and small distance from shore were the reasons to justify this option. Recently with offshore wind farms being built further deep in the sea, the turbine developers accordingly plan to manufacture machines specially made for offshore purposes.

1-2 Oﬀshore Operation and Maintenance

Offshore wind operation and maintenance (O&M) resembles the oil industry to a certain extent. Though the experience of the oil industry is handy in terms of technical planning of O&M, the difference in the total project budget poses a challenge. The money invested for an individual project, the energy produced from it is quite less compared to oil industry and hence offshore wind industry has to develop maintenance schemes under those limited budget constraints. Also, the maintenance management for wind turbines (WT) aims on one hand at reducing the overall maintenance cost and on the other hand at improving the availability [1].

Ashish Dewan Master of Science Thesis 1-3 Fraunhofer MAS Project 3

Moreover, as compared to onshore wind farms, even small failures will have major impact on the overall availability of the wind farm. The contribution of the OPEX to the kWh price is approximately 25 to 30 % which is about 10-15% more than onshore. Equipment downtime costs include spare parts purchasing, repair labour, transportation, crane rentals and energy production [16]. Further, costs for corrective maintenance are a factor of two higher than that of preventive maintenance, whereas for onshore, there is not much diﬀerence [15]. These above mentioned points make the operation and maintenance of prime importance to the oﬀshore wind farm with certain limits of availability.

1-3 Fraunhofer MAS Project

Offshore wind industry is still in its learning process. The project owners take an assurance in the form of a contract that their farm will operate under certain high limits of availability. Hence, modern offshore wind turbine manufacturers are compelled to operate their wind turbines with an availability of nearly 95%. To achieve such levels, considerable amount of additional maintenance work and costs are necessary. There is a substantial scope for optimizing reliability and maintenance procedures. One of the possibilities is to systematically make use of available knowledge and past experience. The consideration of various parameters such as weather conditions, power prognostics, stock keeping, etc. is essential for optimal decisions. Such complex inter-related models require the use of sophisticated tools. Fraunhofer IWES is in process of making such a tool. It is a Multi-Agent-System (MAS), a new discipline in the world of Artificial Intelligence (AI) and Data Mining (DM). It enables to observe and deduce the hidden knowledge and logical dependencies of a great amount of data using appropriate algorithms.

MAS consist of five closely interconnected modules- Failure-Rates Module, Weather Module, Production Module, Logistic & Service Module and finally the Cost Module. The same can be seen in the Figure 1-1. This separation provides the option of using different simulation methods as well as an easy extension [1].

Figure 1-1: Fraunhofer IWES MAS Module [1]

Master of Science Thesis Ashish Dewan 4 Introduction

This research focusses on Logistics & Services for which a probabilistic model is implemented in MATLAB. The topic of Logistic and Services is quite vast. Though certain information w.r.t inventory data, weather module and production module is not available in its current space, maximum aspects of logistics and services are modelled to estimate the most suitable strategy for a given WF.

1-4 Economic Understanding

In Economic terms, the initial project investment is termed as CAPEX and the operation cost for maintaining that project is characterized as OPEX. The total project cost is the sum of CAPEX and OPEX besides the decommissioning cost at the end of the lifetime of WF. When estimating cost of a project over its lifetime, the same is designated as Life Cycle Cost (LCC). Considering wind energy market, the LCC is the summation of- Development and manufacturing cost, Life Support Cost (operating cost) and Phase-out cost. This research deals with only the Life Support Cost of an oﬀshore wind energy system. Life Support Cost (LSC) can be further divided into two components- Maintenance or Services of the wind farm and Spare part logistics. As explained before in Section 1-3, this is a part of Fraunhofer MAS which is named as Services and Logistics Module. Some of the typical variable costs involved in Logistics and Services are explained in Section 3-7.

1-5 Research Objectives

1-5-1 Research Aim

The aim of the thesis is to model and analyse the wind farm maintenance and support organisation. Optimal ordering policy parameters and improved service strategies are aimed for, thereby achieving maximum cost eﬃciency.

1-5-2 Research Questions

Some of the research questions that will be dealt are classiﬁed as follow:

• What are the common reasons for failure of diﬀerent WT sub-assembly?

• What are the best policies for stock-keeping for the wind power systems modelled?

• What are the best access and crew strategies when implementing a replacement operation?

Under this work, development of models and solutions for multi-echelon spare parts management in wind turbine industry will be performed. This includes demand estimation of the spare parts, computation of downtime and availability of the wind farm and ﬁnally estimating the overall life support costs.

Ashish Dewan Master of Science Thesis 1-6 Approach & Methodology 5

1-6 Approach & Methodology

1-6-1 Research Approach

To fulﬁl the objective of this thesis there are three main components needed; understanding of failure and demand behaviour of spare parts, a support organisation model and an optimisation tool to realise the theoretical design. The research approach can be summarized with ﬁve method elements, shown in Figure 1-2.

Figure 1-2: The working process

1-6-2 Literature Studies

Literature studies are concentrated on two different scientific areas, WT technology and optimization theory. Basic WT sub-assemblies are studied besides understanding the reason of its failure and what procedures are followed when a severe fault occurs. Also, critical spare parts and components of a WF are short-listed. Further, to help develop a basic logistic model, inventory theory applicable in general is understood. Ordering policies applicable to a multi-echelon support structure as in the WF industry is detailed. For implementing the service model, failure and spare part demand behaviour, MTTR modelling and maintenance activities are discussed. Also, already existing logistic and service models are summarized to finish off the literature studies.

1-6-3 Field Studies & Data Processing

As mentioned before, the research is not focussed on any particular WF. However, two existing and running offshore WFs (Egmond aan Zee and Nysted) are referred for acquiring information regarding its logistic and service structure. Since, the two named WFs are demonstration projects of Netherlands and Denmark respectively, sufficient information is available for research in the field of wind energy. Further information on WT failure or spare part demand and mean time to repair are gathered from the Fraunhofer WMEP database. Lastly, the weather data is retrieved from two MET masts -FINO 1 and FINO 2, from which the necessary wind-wave time series are prepared. For each of the field studies performed, the raw data is processed in order to be in line with the stochastic model requirements.

Master of Science Thesis Ashish Dewan 6 Introduction

1-6-4 Theoretical Logistic & Service Model

Besides acquiring relevant and useful data, it is necessary to conceive an algorithm and a theoretical design pertaining to the scope and the assumptions deﬁned for both the logistic and service model. The same is done before implementing it in a computational language like MATLAB.

1-6-5 MATLAB Modelling

When modelling complex systems, it is inevitable to use a computer software. MATLAB, which is a standardized and accepted industry tool is used for solving the O&M problem. The objective of both the logistic and service model is to compare diﬀerent strategies and provide the best results in terms of availability and life cycle support costs of the WF.

1-6-6 Veriﬁcation & Analysis

With the development of any new simulation model, need of its verification and validation is important. Both the logistic and service model are tested for different scenarios in terms of sensitivity analysis, comparison studies and extreme value testing. Finally, optimal results are generated and compared in response to different input strategies chosen by the user.

1-7 Outline of the Thesis Report

Chapter 2 presents the WT sub-assembly and its failure characteristics. The important components of the WT nacelle are short-listed and the need for having an optimized support organisation is conceptualized. Chapter 3 gives a theoretical background pertaining to inventory theory, concepts related to reliability, spare part demand and maintenance and an overview of logistic and service modelling applicable to offshore wind. Further, Chapter 4 and Chapter 5 describe the logistic and service model respectively implemented in MATLAB. Both the models are explained in detail with necessary flowcharts and diagrams. Relevant examples are also provided for better understanding. Chapter 6 discusses mainly the field studies and the data processing required to be passed as input to the O&M model. The report proceeds to Chapter 7 where the validation of the model, testing of the extreme cases and implementation is performed. The report is completed with Chapter 8 providing the conclusions.

Ashish Dewan Master of Science Thesis Chapter 2

Wind Turbine Technology

This chapter reviews literature pertaining to Wind Energy Industry and causes of failures for various WT sub-assembly. The wind energy industry and the critical parts of the turbine are discussed in Section 2-1-2. The Section reviews failure of horizontal axis wind turbines and, identiﬁes some common causes of failure in wind turbines. A review of the cost-signiﬁcant items within a wind turbine is presented in Section 2-2. In the concluding para of Chapter, in Section 2-3, the need of optimization of support organisation is discussed with an example.

2-1 Wind Energy Industry

A modern wind turbine consists of two or three blades that are set in motion by the passing wind. This rotating motion is either transferred directly to a generator, or through a gearbox which increases the rotational speed into the generator. All the WTs in this study, a gearbox is used, which is the most common type for WT. Another fundamental diﬀerence in WT design is how the blades handle ﬂuctuating winds. There are three types of regulation: stall, pitch and a combination of stall and pitch. With stall regulation, the blades are formed with an aerodynamic structure causing turbulence near the blade at high wind speeds. The turbulence decreases the lifting power of the blades and thereby limiting the rotational speed to acceptable levels. Compared to stall regulation, WTs using pitch regulation have rotational blades. During high speeds, the blades are rotated from the wind, letting more wind through, which decreases the lifting power, and thereby the rotational speed. The WT models included in our study all have pitch regulation [17][18].

2-1-1 Wind Farms

Average capacity of today’s oﬀshore wind turbine is 3 MW. However, a Nuclear plant alone is capable of producing nearly 800-1000 MW of power. If we were to compare the nuclear plant to a fairly average 3 MW commercial wind turbine, it would take about 400-600 wind turbines to equal the nuclear plant’s capacity, provided the turbines run at their maximum

Master of Science Thesis Ashish Dewan 8 Wind Turbine Technology capacities. One WT looks very small in this context but if put together in large groups, i.e. wind farms, they are merged into a power plant of 100 MW or more. Therefore, the construction of the wind farms has led the wind power technology into a new era. When moving from these single scattered WTs to larger production facilities, the maintenance work is simplified. Instead of long travelling distances between a few WTs, service technicians are able to work at one location, performing daily maintenance work on a close range. This also leads to a greater knowledge of a certain WT type and quicker repairs, since a technician is available in the area. A wind farm comprises of number of WTs connected with electric cables, either located onshore or offshore. To minimize the effect of wake losses, the WTs are placed 400-700 meters apart, depending on the rotor size [19]. Offshore wind farms were not really much different from onshore initially, since the same wind turbine technology with slight modifications was being directly applied to the new environment. Earlier in wind farms, low water depths and small distance from shore was the reason to justify this option. However nowadays with offshore wind farms being built further deep in the sea, the turbine developers accordingly plan to manufacture machines specially made for offshore purposes.

2-1-2 Wind Turbine Sub assemblies- Functionality & Failure Characteristics

When analyzing logistics of wind energy system, it is relevant to identify the important subsystems a WT is composed of. In this section, such a literature study is performed. The critical components of a WT along with their reason of failure is mentioned. For the scope of this report, all these components have been referred to as spares. A WT is to a large extent built with standardised items used in many other industrial applications. Therefore there is an open market, especially for the majority of the mechanical items [8]. Figure 2-1 presents the most important components in a WT. Most of the parts or systems addressed in the picture below are explained in the following subsections [2].

Figure 2-1: Modern WT with Sub assembly description (adapted from [2])

Ashish Dewan Master of Science Thesis 2-1 Wind Energy Industry 9

Basically, there are 4 main reasons of failure of any equipment; human error1, Acts-of-God2, design faults and components related failure3. The wind turbine has to be design tested as per the industry standards. However, these design tests cannot accurately predict all the actual environment factors which vary from site to site or all possible reasons that may occur during the operating life of the wind turbine. Thus to assess ﬁeld failure characteristics of wind turbines, it is essential to understand the likely failure behaviour of the turbines when they are exposed to the natural environment. ECN did a study on the failure behaviour of the oﬀshore wind turbines in the Netherlands. The results are presented in Figure 2-2. It is seen that, the blade failures, generator failures, and gearbox failures contribute together over 75% to the costs and the downtime.

Figure 2-2: Causes of Oﬀshore Wind turbine failure in the Netherlands [3]

Hereby, diﬀerent sub-assemblies are described with its functionality, critical components of the assembly, common reasons of failure, actions needed when a failure occurs, and related failure examples from real.

(a) Blades Wind turbine blades are designed to harness power from wind and then transmit the rotational energy to the gearbox through a hub and main shaft. The blades are designed for optimized output and minimum noise and light reflection. The blades are made of fibre glass reinforced epoxy and carbon fibre [20]. Composite materials are often preferred because of its possibility of achieving high strength and stiffness-to weight ratio [17]. Composite material is also corrosion resistant and good electrical insulator which is an advantage in an offshore environment. As a part of annual servicing, the blades bearings are lubricated automatically from an electrically driven unit [20]. The core of a blade is the part that receives the main load. The most important design factor for the core is that it has to be light and flexible and still be able to handle heavy loads [17]. Cracks can occur on the surface of the blade due to the fatigue and lightning strikes. Ice build-up is also known to cause failure of fibreglass reinforced plastic (GRP) blades [3]. The cracks are not affecting the WT to function but they still need to be repaired so that they do not get worse.

1Gap between what is done and what should have been done such as wrong installation of components 2Refers to natural events where the occurrence cannot be reasonably foreseen or avoided e.g. lightening 3Deterioration of equipment in its normal operating context such as fatigue, wear-out, etc.

Master of Science Thesis Ashish Dewan 10 Wind Turbine Technology

When there are sights of cracks or other weakening in the supporting structure, the blade needs to be replaced immediately [8]. It is diﬃcult to evaluate and repair a blade with a broken structure, hence completely new blade is mounted and the old one is discarded. E.g. at Tuno Knob, the blades were required to be replaced entirely after structure failures in the same [21].

(b) Hub The hub of a wind turbine connects the blades to the main-shaft, and transmits rotational force generated by the blades. Hubs are generally made from steel which can be welded or casted [17]. The topology of a wind turbine determines the specific type of hub design to be used on the wind turbine. The hub also consists of spinner and spinner bracket [22]. The blades, hubs and the fasteners are made of different materials. Thus, interactions between these three components in terms of stiffness during variable loading constitute huge operating problems. Modern wind turbine blades have threaded bushes glued into their roots, and are connected to the hub by using bolts [3].

(c) Pitch System For WTs with pitch regulation, the hub provides bearings for the blades allowing them to rotate relative to the hub. Within the hub the pitch system is performing the rotation of blades. Blades can be pitched individually or by a common, central pitch mechanism. Today most of WTs have an individual pitch system, which is either controlled electrically or hydraulically. An electrical pitch uses a slip ring to transfer electric power from the nacelle and out to the hub. If it is a hydraulic pitch system a rotating union is used to transfer the pressure. Some years ago, there were a lot of problems with leak- ages in rotating unions, but during the last couple of years, there have been paramount improvements in their reliability [17][8]. The industry is split with about 45% electric and 55% for hydraulic controls. The advantage of the hydraulic control is that its power density is higher than electrical equipment and it needs fewer components, making it a simpler system [23]. For example in Vestas V90 turbines, changes of the blade pitch angle are made by hydraulic cylinders, which are able to rotate the blade by 95◦. Every single blade has its own hydraulic pitch cylinder [20]. It is important that the blades can be pitched even if the slip ring, rotating union or some cables or hoses fail. Therefore, batteries or hydraulic accumulators are installed in the hub as a backup system [24].

(d) Drive Train Mostly in all WTs, there is a main shaft connecting the rotor to the drive train. A shaft connection prevails between the rotor and the gearbox and further to the generator. They are used to transmit torque within the WT. Shafts are not only under stress from torque load, but also there is a bending load on the shaft. These loads are time-varying, so fatigue of the shafts is an important factor [8]. Bearings are closely connected with the shafts since they are carrying the weight of the rotating shafts. Bearings have an important function for the drive train, as well as for rotating the blades (pitch system) or the whole nacelle (yaw system) [17].

Ashish Dewan Master of Science Thesis 2-1 Wind Energy Industry 11

Poor lubrication, wear, pitting, deformation of outer race and rolling elements are the main reasons of the failures [3]. Problems can also occur with shafts, if they are often operating under critical speed. At some turning speeds, shafts have resonant frequencies, creating vibrations. Since they are heavy, replacement of bearings and the drive train are complicated procedures, where a large crane is needed to lift them up and down the nacelle [8].

(e) Gearbox The gearbox of a wind turbine increases the rotational speed of the main shaft from as low as 15-20 revolutions per minute (RPM) to as high as 1500 RPM which is necessary to drive a generator of the wind turbine. It is one of the heaviest and most expensive components of a wind turbine. A three- stage planetary gearbox is usually utilized in wind turbines [17]. For e.g. in V90, the gear unit is a combination of a 2-stage planetary gear and a 1-stage helical gear [20]. Gearboxes are built up of shafts, gears, bearings and seals, mounted in a metal cover. The weight of the gearbox increases dramatically in relation to the rated power of the WT. The main load a gearbox has to handle is torque of the rotor. This load, as mentioned earlier, is sometimes constant and sometimes fluctuating. It also suffers loads from the generator when it is started. These loads mainly affect bearings, gear teeth and seals, causing them to fail [17][3][8]. To minimize fatigue of gearbox parts, a functional and efficient lubrication system is highly relevant. For e.g. in Vestas turbines, the oil is collected in a tank. From the collection tank, the oil is pumped to a heat exchanger and back to the gearbox. The pumps distribute the oil to the gear wheels and bearings [20]. It has been observed that the under-dimensioned gearboxes have had a large part in WT failures. The reason for under-dimensioned gearboxes can be that the manufacturers do not fully understand the operating conditions. For e.g. OWEZ offshore wind farm in Netherlands had a complete gearbox change programme for all turbines [25]. Another problem with the gearbox is that even if it is only a small component breaking, the whole system needs to be cleaned out and thoroughly tested. With the gearboxes, the replacements are mostly pro-active [26]. Faults with gearboxes are primarily discov- ered within the first two years of operation-as in the case of OWEZ and Nysted Offshore wind farms. If a gearbox last during the first two years, it is likely that it will last for good period of time [25][27].

(f) Generator The generator within a wind turbine converts the mechanical rotational energy from the gearbox into electrical energy. The generator is slightly different from other generating units connected to the electric grid because it works with a power source (the wind turbine rotor) that supplies fluctuating mechanical power (torque) [3]. The most common type of generator today is the induction generator, also referred to as an asynchronous generator. This type of generator is used in modern type of WTs with variable speed [17]. Variable speed allows varying the rotor speed within a wide speed range. This reduces power fluctuations in the power grid system as well as minimizes the loads on vital parts of the turbine [20]. A generator needs to be protected from water, dust and other foreign particles. There

Master of Science Thesis Ashish Dewan 12 Wind Turbine Technology

are two common types of protection, totally enclosed fan cooled (TEFC) or an open drip protection. There are only a few components in a generator that are exposed to electrical or physical stress. The windings in the rotor and stator are sensitive to high currents leading to increased temperatures that are wearing the windings and can lead to a failure. These windings can be replaced but the generator has to be taken out from the nacelle in turn to make this type of repair possible. The generator bearings and diﬀerent fans are subject to an almost constant mechanical wear and have to be exchanged from time to time [17][8]. Also it is observed that that at power frequencies, SCIG is inherently stable, but when connected to a weak grid with an unbalanced three-phase load, overheating and torque pulsations may occur [20].

(g) Mechanical Brakes Mechanical brakes in a WT have two functions. Usually they are used as parking brakes, when power production is down, but occasionally they are used for emergency braking also. The brake system consists of a brake disc, brake pads and callipers [22]. A mechanical brake can be located somewhere along the drive train. There are two main types of brakes, disc brakes and clutch brakes. Disc brakes need a hydraulic pressure, supplied from a hydraulic pump or accumulator, to operate. Springs are often used to activate clutch brakes, using hydraulic or pneumatic pressure to release it [17][8]. Excessive wear on brake linings which happens mainly due to emergency breaking causes brake failure or even ﬁre [3].

(h) Yaw System The yaw system is used to set the nacelle and rotor in an eﬀective position against the wind. A rotating nacelle requires a yaw bearing supporting the load of the nacelle. The circumference of the bearing has gear teeth which are connected to a yaw gear. The yaw gears are driven by electrical motors (called yaw motors), shifting the speed of the pinion conducted to the bearing’s tethering [17][8]. In Vestas V90, four electrical gears with motor brakes rotate the nacelle [20]. The major causes of failure of a yaw system include bearing failures, pinion and bull gear teeth pitting, yaw brake failure, pinion and bull gear teeth wear-out [3]. To limit the wear on the yaw system, yaw breaks are installed to hold the nacelle in place when the WT is not running [17].

(i) Hydraulics The hydraulic system operates the mechanical braking system, the pitching system and the yaw control system. It also operates the on-board cranes and locking systems for canopies and spinners in larger wind turbines. Main components of the hydraulic system include pumps, drives, oil tanks, ﬁlters and pressure valves. The hydraulic system contains hydraulic oil which is put under pressure to move pistons in hydraulic cylinders. This system ensures that pressure is established when the wind turbine starts and it releases the pressure, when the turbine stops. The pump builds up the pressure which is controlled by a pressure sensitive valve to ensure safe attainment of the required pressure level. For eﬀective operation, a reserve pressure steel tank is often included in the system [3].

Ashish Dewan Master of Science Thesis 2-1 Wind Energy Industry 13

Hydraulic pump failures are often caused by contamination of hydraulic ﬂuid, wrong oil viscosity, premature failure of cylinders due to high hydraulic ﬂuid temperature, hydraulic valve failure caused by cavitation, faulty circuit protection devices , and seal failure [3]. Most of the faults occurring with the hydraulic system can be repaired within the nacelle or after the faulty part has been exchanged [8].

(j) Sensors The necessary information about the processes in a WT is collected from sensors placed at critical points. Sensors in a modern WT measure [8]:

• Speeds (wind speed, rotor speed, generator speeds) • Temperatures (oil temperatures, bearing temperatures, electronics temperature) • Position (yaw position, blade pitch) • Electrical characteristics (current, voltage, converter operation) • Fluid ﬂow parameters (hydraulic or pneumatic pressure, oil levels and ﬂow) [17]

All these parameters need to be measured correctly for the WT to function properly. Typical items that fail are encoders, which measure the position of the yaw system and pitch system. Sensors measuring wind speed and wind direction, for example ultrasonic sensors, placed at the top of the nacelle, also fail from time to time.

(k) Control System WTs need an eﬃcient control system in order to produce electricity of the right form and standard, and to ensure a safe and reliable power production. Control systems have two main functions: to monitor item functionality and to operate the WT. The control unit is the core of the control system. These units consist of hardware logic (e.g. thyristors and transistors) and software, deciding what actions is to be taken depending on the incoming information received from the sensors. A controller can be a mix of computers, electrical circuits and mechanical systems [17][8]. The control unit often fails when one of the many circuit boards fails. Most circuit boards are discarded, but if the damage is not that severe, expensive circuit cards are repaired. The control system is mostly kept in the turbines in the form an Electronic Control Unit (ECU).

(l) Electrical System The electrical generation from a WT requires an advanced electrical system. The actual generation performed by the generator is only a small part of the whole electrical system. The three most important parts of the electrical system are namely- power converters, power transformers and ancillary electrical equipment [17][8]. Power converters are devices changing electrical power from one form to another. Con- verters consist of a vast amount of electrical switches which are opened or closed by an advanced electrical control system. Key components of a converter are diodes; thyristors, gate turn oﬀ thyristors (GTOs) and power transistors like insulated gate bipolar transistors (IGBT). All these electrical parts can fail due to short-circuit and the mag- nitude of the damage is diﬀerent from time to time [17]. These electrical switches are mostly replaced when damaged. A transformer is a crucial component in nearly all AC power systems. A transformer changes the voltage of a current. The largest, main transformer in a WT (often referred

Master of Science Thesis Ashish Dewan 14 Wind Turbine Technology

to as the transformer) is used to change the voltage of the generated power into the voltage used in the internal distributing network. This type of transformer typically operates in the range from 5-50 kVA. Power cables from individual WTs in a farm are connected to a central substation where the produced current is transformed again. The last transformation takes place before the current reaches the high voltage network operating at 60000 kVA. In a WT there are also smaller transformers (often referred to as power supply) which are used to step down the incoming current into a voltage suitable for all the electric driven components in a WT e.g. electric motors, lights, control systems and monitoring. There are several reasons that can cause a failure in a transformer. The most common faults are due to deterioration of the insulation caused by heat, acidity and oxidation [17]. Because of their failure modes transformers can seldom be repaired. Several other components are included in the construction of a WT, which comprises of high and low voltage items. Power cables and slip rings can easily be worn out in case it needs repair. The slip rings are rotating electrical contacts used between the generator and the converter. There are also many circuit breakers and fuses which are opened, if the current gets strong. Fuses must be replaced when used for a long and circuit breakers can be reset after being switched on or oﬀ. Finally there is a main switch which is closed during production, and is only used when maintenance work is done on the electrical system [17]. Overall the sub-assemblies can be summarized in table 2-1. Each sub-assembly have various components referred to as spares in this thesis.

Table 2-1: Wind Turbine Sub-assembly

Sub-assembly Main Components Blades Blades, Blade bearings Hub Rotor hub, Spinner Pitch System Pitch motors, Pitch Bearings Drive Train High speed shaft, Low speed shaft, Main bearings, coupling Gearbox Gearbox shaft, Gearbox bearings, Oil ﬁlter Generator Generator windings, Generator brushes, Generator bearings, Generator fan Mechanical Brake Brake disc, Brake calliper, Brake pads Yaw System Yaw bearing, Yaw drives, Yaw ring Hydraulic System Hydraulic pump, Hydraulic lines Sensors Ultrasonic Sensors, Anemometers, Vibration switch, Temperature sensors Control System Electric controls, SCADA Electric System Electrics, Transformer, Inverter

Ashish Dewan Master of Science Thesis 2-2 Cost Signiﬁcant items within a wind turbine 15

2-2 Cost Signiﬁcant items within a wind turbine

Now that we know the critical components of a wind turbine system, this section iden- tifies the most cost significant items, where consequences of their failure will result in significant financial loss. Fig. 2-3 presents a cost breakdown of a typical offshore wind turbine including the foundation [4]. It can be seen that blades, generator and gearbox together add up to nearly 32% of the total cost with the tower contributing approximately 12%. Hence these are the cost significant spares of a wind turbine.

Figure 2-3: Relative cost for the main components of an oﬀshore wind turbine [4]

It has already been discussed before in Figure 2-2, that the blades, gearbox, generator, yaw system, hydraulic system, electrical and control systems are the major causes of failure of wind turbines. Therefore, it is crucial to concentrate on these sub-components or spares in our analysis for spare part optimization of the inventory.

2-3 The Eﬀect of Logistic & Service eﬃciency on WT availability

The operational availability (A) of any technical system signifies the efficiency of the support organization [28]. It is known that reliability only considers the failure rate of the sub-system, whereas availability also considers the time for which the sub-system was broken. In case of a system providing a service or is producing something, for example a wind power system, availability also means profitability for the owner. When the system is down, due to some sort of failure, the owner can’t sell their services [8]. Here, we introduce another way of defining the operational availability which includes the logistic, weather and the shift delay as well [16].

MTBF A = (2-1) MTBF + MDT where MTBF is the mean time between failures, MDT is the mean-down-time. MDT usually consists of three parts: MTTR (mean-time-to-repair), MLDT (mean-logistics- delay-time) and the MWT (mean waiting time). MTTR is the hands-on time used to

Master of Science Thesis Ashish Dewan 16 Wind Turbine Technology

restore the system to the operational state the spare part is made available. MLDT represents the waiting time for spare parts and materials and the MWT is the delay caused due to weather and the shift delay of the service personnels. These metrics can easily be monitored and estimated in practice, as they are widely used in the equipment industry [16]. MLDT and MWT can occur sometime at the same time, but at steady state they can be considered as independent variables. The preventive maintenance or the planned maintenance also adds to this unavailability and can be added to the above equation as a time component in the nominator. MTBF is now replaced by mean time between maintenance (MTBM), which now includes the average time between corrective and preventive maintenance [28]. A problem is that the improvement in the MTBF and MDT is expensive. MTBF can mainly be affected by investing in more research at the manufacturing and the design level. Also condition based maintenance (CBM) can be done to regularly inspect the sub-assemblies and detect the problems before its breakdown. The MDT relies on the spares stocking level, repair time, fleet size, and usage. Hence, compared to MTBF, there are more ways in which a support organization can affect MDT [8][16]:

• Faster transportation of staﬀ and items • Shortening of lead times • Stock Optimization • More working hours of the crew

Now, let us understand in particular the effect of MLDT and MWT on the overall operational availability of the system- ‘A’. Consider a wind turbine, where no preventive maintenance is performed. It only follows the principle of corrective maintenance. The WT is a stand-alone system and is expected to be working all the time, that is 8760 hours per year. On an average there are roughly fifteen failures per year, which takes about two hours to repair on an average. These do not include any restarts and specifically requires the crew to travel offshore. In the first case, we do not consider any delay due to spare parts and the travel, weather & shift delay. Hence we have:

MTBF = 584hrs., MT T R = 2hrs. (2-2)

From the equation 2-1, the availability of the WT is:

A1 = 584/(584 + 2) = 0.9965 (2-3)

In the second case, we shall also consider the delays such as waiting for the working shift of the technician and the availability of the spare part in its stock. When a WT failure occurs, a service technician requires a spare-part for the replacement. It is found that in 70% cases, the spare parts are in the stock and the delay in that case is zero

Ashish Dewan Master of Science Thesis 2-3 The Eﬀect of Logistic & Service eﬃciency on WT availability 17

hrs. Also, there is a 25% chance that the service team has to wait twenty four hrs. for the speciﬁc spare part to be delivered from another stock. In ﬁve percent of the cases the spare part needs to be ordered from the manufacturer, with a one week lead time (7 ∗ 24 = 168hrs.). Hence the total MLDT = 0.70 ∗ 0 + 0.25 ∗ 24 + 0.05 ∗ 168 = 14.4hrs. The service team is located twenty minutes from the WT. They work only during the day time (twelve hour shift) which makes the mean waiting time for a service technician to be available for six hrs. MTBF and MTTR are the same as above.

MWT = 0.20 + 6 = 6.2hrs. (2-4)

The availability of the wind turbine in this case is:

A2 = 584/(584 + 14.4 + 2 + 6.2) = 0.9788 (2-5)

We can observe that in a more realistic situation, with real estimates of delays, A drops by approximately 2%. This means that the WT was down for more number of hours leading to Loss of Production (LoP). This clearly shows that the waiting time of the spare parts and the efficiency of the service team can substantially affect the performance of the WT. The above example lays the foundation for the need of an optimized logistic and service framework. The next chapter introduces the basic terminologies of inventory management and the related literature in the field of operation and maintenance of offshore wind farms.

Master of Science Thesis Ashish Dewan 18 Wind Turbine Technology

Ashish Dewan Master of Science Thesis Chapter 3

Theoretical Background

The research pertains to the development of two models, namely, a Logistic Model and a Service model. The objective of the former one is to optimize the inventory ordering parameters minimizing the overall inventory cost. The later model explores different service strategies while performing replacement operations; calculating the associated downtime, availability and cost. A typical lay-out of an offshore wind farm and its relevant offshore and onshore logistics needed are sketched in Figure 3-1. The wind farm consists of a number of wind turbines located far off the coast in sea. With the present trends, the planned wind farms will have at least an installed capacity of 300-500 MW with distance of more than 30 km from shore. With the introduction of new and innovative turbine designs, relevant advancements are also being proposed in the way the operation and maintenance of these turbines are undertaken.

Figure 3-1: Typical Layout of an Oﬀshore wind farm with Support Organization [5]

This chapter talks about the basic theoretical concepts in order to develop a logistic and service model for an oﬀshore wind farm. Section 3-1 highlights the basic maintenance activities associated with wind turbines. Further, a typical multi-echelon system is explained in section 3-2. Moreover, for any O&M model, there is a certain mathematical procedure for failure or demand estimation which is described in Section 3-3. Section

Master of Science Thesis Ashish Dewan 20 Theoretical Background

3-4 and Section 3-5 covers the concepts of ordering methods and the choice of inventory management respectively. Then, the basic concepts related to O&M, the MTTR estimation and economic parameters related to the thesis are handled in Section 3-6 and 3-7 respectively. To conclude, some useful and related logistic and service models are discussed in Section 3-8 which helped in designing the O&M model for this research.

3-1 Maintenance Activities

There are two basic types of maintenance activities dealt within this thesis, namely; Corrective and Scheduled maintenance. These are described as follows:

• Corrective Maintenance It is described as the ‘maintenance carried out after fault recognition or degradation and is intended to put an item into a state in which it can perform a required function’ [23]. This strategy is also associated with part replacements but it is not based on systematic inspection by monitoring of the system as in predictive maintenance. In other words, as soon as an item has failed, the maintenance operation is initialised, that is the system is allowed to run until failure, and then the failed equipment is repaired or replaced. It has already been stated that the failure of any mentioned spare would lead to the unavailability of the wind turbine. Further, the repair strategy for a corrective maintenance will diﬀer depending on which class (A, B, C and D) of repair has occurred (Section 5-1).

• Scheduled Maintenance Scheduled maintenance refers to ‘maintenance actions that are typically scheduled in accordance with the wind turbine’s manual, designed by the manufacturer’. This type of actions is planned with respect to the elapsed operation time since previous services or regarding the number of elapsed cycles per revolutions, for the case of components with moving parts [22]. The scheduled maintenance increases the system reliability and performance. It is however presumed here that the scheduled maintenance does not cause any improvements in the wind turbine’s reliability. The assumption here is, to achieve the reliability modelled by the spare part demand in the model, scheduled maintenance must be performed. Further, with scheduled maintenance operations, the turbine needs to be stopped and hence there will be downtime and loss of production caused. That is included to calculate the overall Availability of the system. The scheduled operations are performed cyclically, once a year during low production periods. The detailed discussion for the strategy implementation and the values chosen are done in Chapters 4 and 5 and Chapter 6 respectively.

Ashish Dewan Master of Science Thesis 3-2 Multi-Echelon system optimization 21

3-2 Multi-Echelon system optimization

The support system of any organization can be formulated in diﬀerent ways. In the simplest forms, there is a facility at which the spare parts are stored and repaired, termed as a depot. The role of the depot is to also keep the technical system under the working condition. In the case of wind energy systems, the scenario is quite uncommon, especially when operating a number of larger WTs. Modern WTs are equipped with advanced control systems and electrical system, that may require high skills and special tools to repair. These items are often sent back to the manufacturer for their repair. This also includes main components such as gearbox, generator and transformer. Large items such as blades are hard to accommodate in a local depot and therefore often stored at a central warehouse of the WT manufacturer. If spare parts are repaired and stored on multiple levels within the support organization, it is called a multi-echelon system [8].

Figure 3-2: A typical layout of a Multi-Echelon System

The Figure 3-2 demonstrates a two-echelon system, where the technical system consists of two operating systems, that being the two wind farms. Two local depots are supplying spares to each of the wind farms. The central workshop procures spares from the external supplier from where they are further distributed to the individual depots.

3-3 Spare Part Demand

When there is a failure (replacement only), a spare is required. If earlier failures have no inﬂuence over the probability of a new failure, the time between failures is said to be exponentially distributed. This type of distribution is called a Poisson process. Con- sidering constant failure rates, there is only one important parameter, that is, average demand (D) during the time period T. The parameter D represents both the mean and variance of the distribution. The Poisson probability distribution is given by [28]:

Master of Science Thesis Ashish Dewan 22 Theoretical Background

DT n p(n) = e−DT (3-1) n! where n is an integer (n = 1, 2, 3,...)

In real life, the wind turbines spares have a random stochastic behaviour. That is, the items experience a certain wear-out with time, including a period of infant mortality [28]. This gives the classic Bathtub curve, with an increased probability of failing at the beginning and at the end of the lifetime and a lower, constant failure rate, in the middle. (Figure 3-3).

Figure 3-3: Universal Bathtub Curve

For a farm with a lot of items in operation in several WTs, the actual accumulated operational time for each individual item may be very diﬀerent due to diﬀerent starting dates, failures and replacements. So even if each spare’s failure rate is following a bathtub curve, the total demand has an almost constant rate (Poisson distributed) [29]. Hence, for corrective replacements, a stationary Poisson based demand is assumed at the depot. The explanation for the maintenance plan, the working strategy, and the corresponding values chosen with its associated data processing are discussed in later chapters.

3-4 Diﬀerent Ordering Policies

Spare part demand arises when a component of the wind turbine fails. The control room at the depot records the need of spare. In a situation when the depot does not have the required spare, it is transported from the Central Workshop (CW). Again, with the spare part not being present at CW, the same is ordered and obtained from the external supplier. This ordering procedure in general can be done following two policies, which are explained in terms of inventory theory as follows:

• Batch Ordering (R,Q) Policy When using this policy, an order is placed when the inventory position reaches or goes below the reorder point, R. At each order, a batch of quantity Q items is

Ashish Dewan Master of Science Thesis 3-5 Single-Item vs. Multi-Item Inventory Optimization 23

ordered. Several batches can be ordered at the same time, if one batch would not be suﬃcient to make the inventory position reach over R. Therefore, the policy is sometimes referred to as (R, nQ) policy. In case of continuous review, the reorder point R will always be reached exactly if the demand is one unit at a time. In the case with discrete demand process, such as a Poisson process, the inventory position lies in the interval [R + 1; R + Q]. On the other hand, if the demand is for more than one item or if the review is periodic, the inventory position can fall below the reorder point before an order is placed. In these cases the ordering of more than one batch can be in question. Consequently, it is not certain that the inventory position reaches R + Q after a new order has been made [30]. • Base Stock (S − 1,S) Policy The (S − 1,S) policy also called as the Base Stock Policy is a special case of the (R,Q) inventory policy were the reorder quantity is taken as one. It is usually used for slow, expensive items and when holding and backordering costs dominate [31]. Furthermore, when there is continuous review, an order will be made each time when a demand has occurred. The amount of items ordered will be having the same number as the number of demanded items, which means that the inventory position will always be S. In case of periodic review, items will be ordered at all times except when the demand has been zero since the last inspection. This policy is also called S policy, order-up-to-S policy or Base Stock Policy [30]. If s = S − 1, the S policy is equivalent to the (s, S) policy and if R = S − 1 and Q = 1, it is the same as the (R,Q) policy. [30]

Since, the research discusses the low demand, critical and expensive items; the stocks are managed according to the base stock policy (also called as S − 1,S policy) both at the depot and the Central Workshop. Further, the stocks at the central workshop can be procured following the (R,Q) policy as well. This is more suitable if more than one depots are handled by the same CW. Such a possibility is discussed in one of the veriﬁcation tests in Chapter 7.

3-5 Single-Item vs. Multi-Item Inventory Optimization

Spare part or inventory optimization can be approached in two ways. First, considering a system-level approach, were the goal is to look at optimal system cost eﬀectiveness. However, the second approach,item-approach; aims for the determination of optimal stock amount per individual spare without considering the other items in the system. The key points of both the approaches are explained below:

• With single-item approach, the stock levels are optimized for every single stock keeping unit (SKU) separately whereas, a multi-item approach optimizes the stock levels for all SKUs at the same time.

Master of Science Thesis Ashish Dewan 24 Theoretical Background

• The single-item model can be used to override ﬁll rates for critical items, to ensure availability against an increased cost. This might be necessary because the multi- item model only optimizes the aggregate ﬁll rate against minimal cost, whereas for some items it might not be accepted that the customer has to wait [30]. • Multi-Item approach is said to have a better performance in terms of the total costs of the system because it weighs stocking more cheaper [32].

Example: Consider one warehouse with just two parts, A and B with an equal demand. Suppose a service level of 90% fill rate is necessary to be achieved. In a multi-item model, overall fill rate of 90% can be reached by storing fifteen items of A and three items of B. Using a single-item model, it is necessary to have at least nine items of both A and B. Single-item modelling is followed in this research since the optimization is to obtained for slow moving critical and expensive spare parts of the wind industry.

3-6 MTTR Modelling and Estimation

MTTR is defined as the average time necessary to troubleshoot, remove and replace a failed system component [33]. In this thesis research, it is the online repair time incurred by the crew for the replacement of the faulty spares. Analysis and estimation of MTTR has been done for German wind turbines [34] and Swedish turbines [35] in two independent researches. The results of the research cannot be directly applied in this thesis mainly because of three reasons. First, the study was done for onshore turbines, were most of the turbines are in range of 500 kW. Second, the analysis has been done for the entire sub-assemblies (as discussed in Section 2-1-2). Third, there is no conclusive difference between the logistic, weather, shift delays and the actual repair time. Therefore, in some of the cases the repair time is overestimated as the total mean downtime (MDT) is used to estimate the MTTR. The distribution most commonly used to describe the actual frequencies of occurrence of system repair time is the Lognormal because it reflects short duration repair time, a large number of observations closely grouped about some modal value, and long repair- time data points. The general shape of log normal distribution is shown in Figure 3-4 [33]. A stochastic input considering both mean and standard deviation is provided to the model as offshore. The MTTR can vary depending on the weather conditions. The raw data treatment and the corresponding inputs to the model are discussed later in detail in Section 6-2-2.

3-7 Economic Parameters

Logistic and service optimization needs to be performed within certain budget limits. The best service levels have to be maintained with lowest possible support costs. Some of the typical economic parameters that are often mentioned in inventory management

Ashish Dewan Master of Science Thesis 3-8 Logistic and Service Models: Existing Models & Theory 25

Figure 3-4: Lognormal Distribution for estimating MTTR

are discussed below. These cost estimators are used for calculating the variable support cost and the objective is to minimize these parameters with an optimized support organization.

• Item Consumption Cost is the total capital cost of the spares that are consumed over the lifetime. • Holding Cost/Storing Cost is money spent to keep and maintain a stock of spares in storage. It includes space, insurance, protection, capital costs, etc. They are either expressed as proportional to the value of a spare (per item) or proportional to the stock level (a constant cost per item). • Reorder Cost are usually the ﬁxed costs associated with replenishment (independent of the batch size) [30]. It includes transport, administration and set-up cost. • Backorder Cost/Shortage Cost is the money lost due to a situation when a spare is demanded and cannot be delivered due to its non-availability [30]. This could be in terms of loss of production (LoP) of wind turbines, increased transportation costs due to emergency shipment, etc. Similar to holding costs, they are usually considered in proportion to a value of a spare (per item). • Corrective Replacement Cost are part of the service costs for the replacement of faulty spare part with a new spare part. They are the summation of cost for every mobilisation (man hrs. cost + access vessel cost per hr.). • Preventive Replacement Cost are again part of the service costs for the replacement of faulty spare part with a new spare part. It is the summation of costs for every mobilisation (man-hrs. cost + access vessel cost per hr.).

3-8 Logistic and Service Models: Existing Models & Theory

As mentioned in the introduction of this chapter, the O&M model is separately modelled as a Logistic and a Service model. This section gives a brief literature review of both the models. The research on O&M is performed by building models which depict an oﬀshore wind farm support organisation. SINTEF Energi AS in one of their studies give an exclusive

Master of Science Thesis Ashish Dewan 26 Theoretical Background

summary of the forty eight such existing models both available commercially or as pilot projects [36]. ECN, TU Delft, Risø and NREL have developed several models for the estimation of total project costs as well as for operation and maintenance. These models are well documented in several publications even though the detailed description of the software is always confidential. However, the concept of the different models is described in such a manner that it is possible to adapt these models. ECN in their OMCE calculator provide a separate option for implementing a spare part optimization, but the procedure as such is not explained in detail. Besides the research institutions specified, large consultancies also have tools to simulate aspects of offshore wind farms, but these models are usually not available outside the company. Some of these consultancies are Garrad Hassan, Ecofys, Det Norske Veritas (DNV) and BMT. The MWCOST tool from BMT, a consulting firm, illustrates a detailed approach to modelling of logistics and offshore operations which also includes all aspects of operation and maintenance. Being specific to the logistic model, inventories are maintained for several purposes which include economies of scale, demand uncertainty and lead time, to foresee on an expected rise in value or to smooth out the demand irregularities and to keep the control on economic parameters. Inventory control applies different models to counter these tasks. The emphasis of this research is the spare part inventory control, which is a special case of general inventory. Unlike the manufacturing inventories, the purpose of spare part inventories is to assist a maintenance staff in keeping equipment in operating condition. Spare parts are not intermediate or final products to be sold to the customer [37]. Moreover, the policies that govern the manufacturing inventories are different from the spare parts inventories. For the former, the inventories are managed by increasing or decreasing the production rates, changing the schedules, improving the quality and reducing lead times. However, in the latter, the purpose is defined based on the way the equipment is used and how it is maintained. The solution for any spare part inventory model depends on the way it is characterised. As mentioned above in Section 3-3, the demand is assumed to be stochastic following a Poisson distribution. Also, the wind industry is a multi-echelon system with a depot and a central workshop were single-item approach is followed at each of the inventory. Each spare part is considered as separate and the inventory optimization is done according to that. The goal of all models is to advise upon the optimal answers to the questions concerning spare part inventory control. The multi-echelon technique for recoverable items (METRIC), model developed by Sher- brooke [28] is the basis for a large number of multi-echelon models. The objective of METRIC is to minimize the sum of backorders across all the depots. Sherbrooke and Graves together implemented a better version, named as VARI-METRIC. It was found that by employing the latter, an increase of 10% is achieved in the overall optimization. Similar to the METRIC, this research does follow the base stock or the (S −1,S) or the base stock policy. The inventory costs fall into three categories: holding costs, order costs and backorder costs. For a (S − 1,S) review system with the spare parts being expensive and having a low demand, the holding and the backorder costs influence more than the order costs [32]. Hence, the latter is ignored for this research. For mathematical models, if the relations are not that complicated, an analytical solution can be obtained. But with complex models, the model needs to be examined

Ashish Dewan Master of Science Thesis 3-8 Logistic and Service Models: Existing Models & Theory 27

through simulation. Mathematical models that are examined by simulation can be categorized in diﬀerent ways. Firstly, whether the simulation model is a static or dynamic model. Secondly, if it is deterministic or stochastic and thirdly, if it is continuous or discrete model [38]. The logistic and service model for this research is a discrete, dynamic and stochastic. The next two chapters discuss the proposed Logistic and Service model.

Master of Science Thesis Ashish Dewan 28 Theoretical Background

Ashish Dewan Master of Science Thesis Chapter 4

Logistic Model

The previous chapter provided an overview of the basic terms often used in logistic and service optimization. Also, a brief summary is provided on the existing O&M models. This chapter will use the discussed theory and develop a logistic model for the optimization of spare part stock levels to achieve a suitable cost value of the support organization. Firstly, in Section 4-1, the global assumptions relevant to both logistic and service model are listed. The O&M model framework is described further in Section 4-2. Next, the logistic model characteristics and scope are discussed (Section 4-3). Finally, the last four sections, 4-4, 4-5, 4-6 and 4-7 provides an overview of logistic model, the inputs required, evaluation and optimization for a single item respectively.

4-1 Global Assumptions of the O&M model

In general, the models used are a simpliﬁed version of the real situation. This means, certain assumptions or delimitations are made. The complexity of any model depends on the assumptions that are made in order to build the same. It is preferred for the model to be close to reality but at the same time, it becomes more complex and diﬃcult to solve. Following are certain global assumptions for the O&M model:

• Turbine Selection The number of oﬀshore turbine manufacturers have increased in the post recent years. But, however, Vestas and Siemens are still leaders in the market of turbine industry. So, sub-components of only turbines manufactured by Vestas and Siemens are analysed for preparing the spare part list. Moreover, most of the projects in pipeline will have turbines manufactured and installed from the said manufacturers. Speciﬁcally, Vestas V80, V90 and Siemens SWT 2.3, SWT 3.6 are chosen for this thesis study. • Selective choice of WF parts The wind farm (WF) consists of wind turbines (WT), foundations, cables and

Master of Science Thesis Ashish Dewan 30 Logistic Model

offshore high voltage sub-station (OHVS). Though the focus should be the entire WF, but in this model, only Nacelle components of the WT are considered. The reason is that in practice the responsibility for the Operation and Maintenance (O&M) of turbines, foundations and cables is with different organizations. Also, there is hardly any historical data for the foundation repair and cable failure. • Selection of Spares Although a WT possess hundred to thousand spares, but for the purpose of this model only first indenture spare parts are considered. These spares are critical for the operation of WT and it is assumed that the failure of any of these spares will result in unavailability of the WT. Moreover, a deliberate attempt is made to include the complete sub-assembly of the Nacelle. Furthermore, for offshore WT, onsite repairing of lower indenture spares would take longer time. This results in higher system downtime. Therefore, it is preferable to stock the inventory of higher indenture items. • Failure & Demand Interchangeability The terms failure and demand are used simultaneously. It is considered that when there is a failure, demand for spare arises. Moreover, the raw failure data is filtered to only include instances where a spare is required and hence used for spare demand prediction. Therefore, if no spare is on hand, some system has a ‘hole’ and the WT is unavailable until a spare is supplied. • Turbine manufacturer as the service provider Maintenance procedures and inventory management is a task which requires enough technical know-how of the turbine nacelle. Also, the operators of the already installed wind farms in Netherlands have chosen to extend their contracts after their five year warranty period. Therefore, it is assumed that long term contracts are signed with the turbine manufacturer for the O&M of the wind farm.

4-2 O&M Model Framework

The design of logistic and service strategy for the O&M of oﬀshore wind farms depends on what suits best to both the wind farm owner and the turbine manufacturer. Sometimes geographical locations, environmental limitations and budget constraints encourage developing strategies in a certain way. This section introduces one such common framework practised in the oﬀshore wind O&M industry. The said structure of implementing O&M is considered throughout the thesis work.

Ashish Dewan Master of Science Thesis 4-2 O&M Model Framework 31

Figure 4-1: Model Framework implementing Logistics & Services for an oﬀshore wind farm

The model framework shown in Figure 4-1 is understood as follows:

• The O&M of the offshore WFs comprises of two aspects- (a) Logistics and (b) Services. • The logistics concentrates on managing the inventory, that is, procurement of spares in the right quantity and at an appropriate time. Also, it has a function of providing the spare when a replacement of the component is to be performed. It is a multi-echelon system with the inventory being managed at more than one place. The immediate logistic supply is provided by the depot (Figure 4-1). If a particular spare is not present at the depot at the time of demand, the part is shipped from the CW In the extreme case of spare not being present even at the CW, it is ordered from the external supplier or the turbine manufacturing department. In most of the cases, the CW is itself the manufacturing facility. • The services focusses on the implementation of maintenance operation on the turbines. This could be as small as performing the regular activities like greasing, oiling and cleaning or major operations like replacement of spares. The service team has to travel offshore to the WF with the aid of different access vessels. In case of replacements, the service can only be implemented after the spare part and the access vessel is acquired. • The downtime associated with the logistic supply of spare part is MLDT. Further, while performing the service operations there are various unwanted delays like weather, shift, travel, planning, access delay. These are collectively termed as

Master of Science Thesis Ashish Dewan 32 Logistic Model

MWT. Finally, the net time spent by the crew repairing the turbine is designated as MTTR. An account of each of the delay with an example is already provided in Section 2-3. In all, the summation of the delays is called as Total Downtime.

4-3 Logistic Model Scope, Delimitations & Characteristics

The logistic model for an oﬀshore wind farm support organisation is designed within some constraints. The next subsections discusses the aim, limitations and attributes of the logistic model respectively.

4-3-1 Scope of the Logistic Model

The goal of the stock keeping model or the logistic model is to determine the order up-to levels (S − 1,S) at the local depot and the central warehouse. The objective is to have the best possible combination for the optimum total cost. Single-item optimization is done for the multi-echelon system, where maximum ﬁll rate is accomplished within optimized cost range.

4-3-2 Delimitations of the Logistic Model

• No Lateral Supply: In the Figure 4-1, there is an important assumption in the echelon structure: Only tree like structure is assumed wherein each first echelon operating plant (WF) has a specific second-echelon supplier (depot) for requirement of any given spare. Hence there are no parallel shipments i.e. lateral supplies. • No cannibalization: In Industrial terms, it is defined as the removal of service- able parts from a damaged machine (for e.g. air-planes, wind turbines, etc.) and which is used in the repair of other equipment of the same kind. Such possibility is not possible in this logistic model. • No Perishability & No Obsolescence: The lifetime of the spares follow a bathtub curve pattern. There is no immediate perishability considered. Also, the spare don’t obsolete, that is, the state which occurs when an object, service, or practice is no longer required even though it may still be in good working order. • Handling of Excess Demand: When there is a requirement or a demand of a spare at the depot and supposedly if it is not met, then there are two possibilities: (i) the wind farm operators go to another supplier; or (ii) they wait for the spare to replenish from the same supplier. The former is the lost sales case in inventory theory and the latter creates a backorder on the supplier.In this model, there are no lost sales and only backorders are considered. • Crew Availability: Since the cost of providing a technician to each field depot is dominated by the inventory cost of holding the expensive parts and the failure rates of the parts are quite low, a technician is usually available for service whenever a repair is required [39]. This is the same assumption considered for the modelling the logistic scenario.

Ashish Dewan Master of Science Thesis 4-4 Logistic Model Overview 33

• No planned shortage: OWF industry is a service bound industry where high availability levels are to be met. Therefore it is important that there are no such logistic and managerial decisions taken where shortages are planned.

4-3-3 Characteristics of the Logistic Model

• The logistic demand is met through a multi-echelon system. • The local depot and CW both follow (S − 1,S) stocking policy. • All spares are considered as consumables, that is, non-repairable. • Every individual spare demand at all warehouses follows an independent Poisson process with a fixed parameter Λ. • The lead time to the central warehouse is independent and identically distributed random variable with mean Ti0 (exponential distribution with mean value). • Delivery time from the central to the local warehouses is deterministic. • There is infinite supply to the central warehouse (from external supplier). • All warehouses have infinite capacity.

4-4 Logistic Model Overview

To ﬁnd the optimal inventory parameter within the suitable value of inventory cost, a simulation tool in MATLAB is developed. Firstly, a logistic evaluation with the worst case scenario is performed with the following input variables: spare part demand, spare part characteristics, holding and storage cost, etc. Flowchart in Figure 4-2 gives an overview of the inputs passed and the outputs obtained from the model.

4-5 Logistic Inputs

The logistic model requires certain inputs in order to obtain an optimized solution. A graphical user interface (GUI) is built with Matlab enabled GUIDE to simplify user interaction. The GUI with the description of its characteristics is documented inB. Inputs for both logistic and service model are passed through the GUI. However, the inputs corresponding to only the logistic model are documented in Table 4-1.

Table 4-1: Inputs to the Logistic Model

S No Inputs to the model Elaborative Parameters as Inputs Description of each Input 1 Spare Part Characteristics Spare Part Code It is the number by which a particular spare is identified. Spare Part Name It refers to all the selected critical spares which are associated to the working of a turbine. Spare Part Item Price It is the capital price of each spare part which is bought and kept at either of the inventories. 2 Spare Part Demand Demand Rate at Depot/CW It signifies the demand rate of the spare parts per year per turbine for corrective replacements. 3 Transport Strategy Number of Farms It specifies the number of wind farms, the CW is supplying the spare parts to. For more than one wind farm, (R,Q) policy is employed at CW. 4 Lead time for a Backorder It indicates the stochastic or the deterministic time for a spare to be delivered a the CW and depot respectively at the time of a backorder. Cost Parameters Holding Cost It is the cost of storing a spare part at the inventory. The cost is estimated as a proportion of the spare part price. Shortage Cost It is the cost incurred when a backorder occurs at the inventory. Again, the cost is calculated as a percentage of the spare part price.

Master of Science Thesis Ashish Dewan 34 Logistic Model

Figure 4-2: Simpliﬁed ﬂowchart for the Logistic Model

4-6 Single-Item Logistic Evaluation

The logistic model being single-item, can be split into an (S − 1,S) problem for the central workshop followed by a similar problem per item at depot. There is no interaction between the two warehouses. Also, the demand at the depot and the central workshop is same for a single WF. Only the delivery times at each of the warehouses are diﬀerent. Hence, evaluation and optimization only at depot is discussed here. For the overall optimization of the multi-echelon system, similar approach can be followed at the CW also. With the inputs loaded, the model combines the information to evaluate the spare part stock keeping procedure for the depot over the chosen time period. The simulation block is divided into four simulation blocks for which a preview is shown in the ﬂowchart below (Figure 4-3). Evaluation process is done for the worst case scenario, meaning by, the inventory parameter S is equal to zero for all the spare parts. Further, explanation of the evaluation simulation is explained in subsections below.

4-6-1 Operation: Spare Part Demand Estimation

Section 3-3 addressed the theory of spare part demand. Considering a Poisson demand at the depot, the need is divided equally for each spare part throughout the length of the project. Following single-item approach, the requirement of each spare is considered separately following which the operations mentioned below are implemented. As stated before, the spare part demand is equal to the corrective replacements undertaken

Ashish Dewan Master of Science Thesis 4-6 Single-Item Logistic Evaluation 35

Figure 4-3: Flowchart of the Logistic Evaluation Block

through out the lifetime. The procedure followed for the failure estimation and the values chosen for demand estimation is documented in Section 6-2-1 and Appendix C-3 respectively.

4-6-2 Operation: Stock Handling

The purpose of any stock control system is to determine when and how much to order. This decision should be based on the stock situation, the anticipated demand and diﬀerent cost factors [30]. For any inventory, the spares can be categorized into stock on hand, outstanding orders and backorders. However, when determining costs associated only with holding and shortage, one takes the inventory level into consideration, represented as:

InventoryLevel = StockOnHand − Backorders

There are two situations which arise while modelling a Poisson distributed demand. They are listed as follows:

• When a demand occurs at depot, there are further two possibilities. The ﬁrst is when the spare is present at the inventory and is immediately delivered to the service team for the corrective replacement. And the second is when the spare is not present at the time of demand and requires to be backordered from the central workshop. In this case, the service team has to wait for the time equal to the lead time of that spare part.

Master of Science Thesis Ashish Dewan 36 Logistic Model

• In the situation when a spare arrives at the depot, again there are two possibilities. One, the delivered spare is a backorder and needs to be immediately delivered to the service team. Second, the spare is kept in the inventory to reach the order up to level or the base stock level. Corresponding action is taken depending on the stock levels and demand of the spare.

This approach is used for handling the stocks both at the depot and central workshop. In the case of backorder occurring at either of the two inventories, there is a certain deterministic or stochastic lead time for acquiring the spare part. Following the worst case scenario of S equal to zero, backorder will happen in all the situations.

4-6-3 Operation: Inventory Parameter Computation

Inventory parameters are computed after all the spare part demands are handled. Pri- marily, the time in stock and time in backorder is calculated. The principle here is that if the value of S is one and there is no demand of the spare throughout the length of project, the entire period is counted for the time in stock. Similarly, in order to achieve the order up to levels, there is a certain delay of acquiring the backordered spare. This is termed as time in backorder. The summation for the entire demand list is performed to obtain the cumulative sum of time. Speciﬁcally, for evaluation (S as zero), there is only going to be time in backorder at the time of demand of spare part. There is no storage of the spare at the depot. It is always backordered from the CW.

4-6-4 Operation: Inventory Cost

The cost for the system in this simulation is the summation of holding and order cost. This cost will help in evaluating and hence choosing the right base stock values (S) for each of the spare part. The cost of the holding are computed as the accumulated time in inventory for all spares times the holding cost divided by the simulation time. Further, the shortage or the backorder cost is computed similarly as the accumulated time customers are made to wait times backorder cost divided by the simulation time. Note that for the evaluation process of S as zero, the cost will not always be the worst case scenario. It highly depends on the demand rate, holding and shortage cost. It should be noted that the evaluation process is executed for a signiﬁcant number of runs and an average of cost is calculated.

4-7 Single-Item Logistic Optimization

The evaluation procedure described above always considers S as zero. This signifies that there is never a spare part at the depot or CW. However, this is not true in real scenario of offshore wind support organisation. Since we are dealing with highly critical and expensive spare parts, for which the holding and the shortage cost is amble, the value of S is varied from zero to a maximum of five for each of the spares. The entire evaluation procedure is repeated as explained above to obtain a optimum and a cost effective solution for S. The process is done for each spare part in the inventory.

Ashish Dewan Master of Science Thesis 4-7 Single-Item Logistic Optimization 37

Though with a single WF and depot, the demand at both the inventories is same, but the value of S can be diﬀerent considering that the spare part at the central workshop requires more holding costs. The test case in Chapter 7 discusses the impact of lead time at either of the inventories. Note: The optimized base stock values achieved for each of the spare part from the logistic model is further used as an input to the service model. Here, the spares in the inventory at depot and CW are stored according to the suggestions made by the logistic model. Hence, the calculation of MLDT which is featured in the ﬁnal event list (AppendixH) depends on the way the inventory is managed for a particular spare.

Master of Science Thesis Ashish Dewan 38 Logistic Model

Ashish Dewan Master of Science Thesis Chapter 5

Service Model

The preceding chapter explained the implementation of a logistic model for an OWF support organisation. This chapter discusses the other component of the O&M, that is Service aspect. The chapter starts with deﬁning diﬀerent classes of maintenance type (Section 5-1). Further, in Section 5-2, the model scope and assumptions are listed. Finally, the service model overview and their corresponding inputs, simulation and outputs are explained in Section 5-3, 5-4, 5-5, 5-6 respectively.

5-1 Classes for type of Maintenance

The logistic and service arrangement can be different depending on the maintenance operation that needs to be undertaken. In this research, the spares besides being classi- fied as repairable and discardable are also grouped into five categories depending on the requirement of spare and also upon the size of the same for replacement. The categories are made in reference to DOWEC project undertaken by ECN Netherlands [40] and the maintenance actions categorized by a safety report in Norway [41]. The corresponding list is tabulated in AppendixG and the explanation is given below for each of the Class maintenance types.

1. Class A: An imperfect maintenance operation (as mentioned in Fraunhofer MAS project) where there is no requirement of spare part. It involves either the restart of the turbine or securing and lubricating of some parts. 2. Class B: A minimal replacement (as speciﬁed in the Fraunhofer MAS project) of small sized sub-components. New spares are required. Replacement of spares with a maximum weight of nearly 1 Tonnes (1000 kg) is feasible where-in the permanent internal crane is suﬃcient for movement of said spare. Some of the typical spares to be replaced in this category are relays, yaw motor, sensors, etc. 3. Class C: A perfect replacement of medium weight sub-components where-in new spares are required. Replacement of spares with a maximum weight of nearly 50 Tonnes. A build-up crane has to be mounted on a crane vessel for the replacement

Master of Science Thesis Ashish Dewan 40 Service Model

of spares. Some of the spares in this category are gearbox, generator, a single blade, etc. There is a certain lead time associated with the crane vessel as it is not always in possession of O&M operator. 4. Class D: A perfect replacement of medium or large sized sub-components, which require the removal of a rotor hub or the entire nacelle. Again, new spares are required with an arrangement of a jack-up vessel. Huge lead time of the vessel will be involved since the jack-up is not always in possession with the O&M operator. 5. Class E: An annual scheduled maintenance of the turbines under which basic operations like greasing, ﬁlter replacement, etc. are performed. The maintenance operations are planned in low wind and production period.

Note: It has already been explained that in this research work the replacement of spare is the main focus except the annual scheduled maintenance of turbines (Class E). Hence, the Class A repair work is excluded from the initial failure data or spare part demand. In addition to the minimum repair in Class A, there could also be restarts of the turbines which are required mainly for electrical and control systems. This is also not taken into consideration for our study. The maintenance under Classes B, C and D is considered for the corrective replacement, while Class E is only for the minimal scheduled maintenance of the turbines.

5-2 Service Model Scope & Assumptions

The aim of the service module is to model and optimise the right strategy of replacement which could result in high availability levels. Further, the most cost eﬀective crew and transport methodology is derived. Also, the calculation of downtime and the associated service parameters is done. In order to achieve that, it is important to deﬁne the boundaries of such a model. Hence, the sub-section below explains the scope and assumptions of the service part.

5-2-1 Scope of service aspects

It is diﬃcult to model the service aspects, as seen in real sense. The decisions made are often management based and can vary from situation to situation. Moreover, the aim of this service model is to simulate situations, to compare the best crew and access strategy. Also, corresponding downtimes and its eﬀect on the overall availability of the system is obtained. Following are some boundaries under which the service module is modelled:

• There is no estimation of production availability. Only operational time availability is obtained. • There is no consideration of overlapped replacements (as discussed in logistic model). Each event is considered as a separate case of replacement. • There is no optimisation of crew strength and its availability. It is assumed that they are always available and aspects like sickness or oversleep is not considered.

Ashish Dewan Master of Science Thesis 5-3 Service Model Overview 41

5-2-2 Assumptions of the service model

The behaviour and the results of any model highly revolves around its initial assumptions. Likewise in logistic model, service model has certain initial delimitations and are listed as follows:

• Only one input value of the mean distance from the depot to the wind farm is used. No separate consideration is done for each of the wind turbine. • The classes for the type of maintenance (discussed in Section 5-1), remain the same as speciﬁed initially. For e.g. aerodynamic brake is a Class C maintenance and will be replaced always using a crane vessel. • The weather predictions made in the form of building an accessibility vector is always accurate. • The time crews take to change from one turbine to another (especially in the case of scheduled maintenance) is completely neglected and not included in the downtimes. • The crews that perform scheduled maintenance services are the same crews that perform corrective maintenance services.

5-3 Service Model Overview

To find the cost optimum service strategy for a specific offshore wind farm and output critical information such as availability, downtime, visits to farm or operational costs, a computerized tool in MATLAB is developed. A Monte-Carlo simulation is performed for a selected number of runs given the following input parameters: spare part demand, spare part characteristics, crew and access strategy, etc. (See Flowchart in Figure 5-1). The parameters are computed using stochastic processes to replicate the WT failure events. By carrying out a Monte-Carlo simulation, for a reasonable number of runs, the model ensures that all the different scenarios are covered. A description of each of the blocks is given in the following subsections.

Master of Science Thesis Ashish Dewan 42 Service Model

Figure 5-1: Simpliﬁed ﬂowchart of the Service Model

5-4 Service Inputs

As was the case with logistic model, the service model also requires certain inputs for the simulation block to generate outputs. The ﬂowchart in Figure 5-1 already mentions the various inputs to be passed. Again, the GUI documented in AppendixB gives the overview of the entire set of inputs required for the working of logistic and service optimization model. However, for the service model alone, the input parameters with their description is mentioned below in Table 5-1.

Ashish Dewan Master of Science Thesis 5-4 Service Inputs 43 a mean and standard deviation to be inserted. shift patterns, there are two set of crews working in the alternate shifts. decision to travel offshore for their repair work. of the maintenancefollowed. type. Whereas for For Class Class C B and & Class D, Classand full Class E shift D maintenance, pattern failure. is short employed. shift pattern are small tools and spare parts. trip. operate. Service Model Inputs Table 5-1: Starting year of ProjectOperational lifetime of WFNumber (years) of Turbines inNruns WFManipulation ParametersSpare Part CodeSpare Part NameSpare It Part refers Maintenance to Class the Type estimated in-service duration ofFailure Rate the (Corrective WF Replacement) It signifiesMTTR the (Mean year, & It when St. indicates the deviation) the project total is number commissioned. of W.T. installed It in isShift the a Patterns WF corrective It factor indicates used the for It type changing signifies of the the maintenance reliabilityShift failure procedure estimates. Starting rate required Hours defined t perShift be year Ending followed per for Hours turbine aMinimum for given Working a failure. Period corrective (hrs.) replacement. It It is It is the refers the It number to number refers byEfficient the of to which Working time Patterns iterations a all that required is particular the spent to spare selected by run is spares the a identified. which crew Monte- are on Carlo critical repairing simulation or to replacing turbine a operation. Overnight spare. Staying It Provision requires It impliesAccess the Vessel Speed least (knots) numberMean of Distance good from It It weather Port specifies is hours,Lead to the the Time_Spare only WF working time Part pattern (km) after when of from which theLead It the CW the shift, crew Time_ is It to crew whether starts signifies Access the Depot short makes the Vessel working. time (hrs.) shift (hrs.) possibilityAvailability the when of or for the Offshore full choosing crew Accommodation shift. a It end variable In is work their case the shift working of pattern deterministic period. full depending It time on indicates required the to the class acquire facility aWind provided spare Speed It to part Thresholds is the from the CW crew average to for distance depot. between staying the It offshoreWave is WF Height It in an and Thresholds is offshore the the the structure port case speed provided onshore. of for of temporary Class the It arrangement vessel C is of that the the is crew deterministic used besides waiting for storing Scheduled time the Maintenance for maintenance. per the turbine vesselStarting to Month reach of the maintenance portImplementation from of the Special supplier. CaseNumber of Crew on Access Vessel It indicates the wind It speed is It the limitation number implies after of the which predefined significant the hours wave of access regular height vessel maintenance It threshold does on is above not a the which given plan optimum It wind the month their provides turbine. in access an which It option vessel the is of does maintenance the running is maximum not a scheduled. crew special limit maintenance allowed case on using a mother given vessel. access vessel. 1 WF Information 2 Spare part Characteristics 3 WT Reliability 4 Crew Strategy 5 Transport Strategy 6 Weather Thresholds 7 Scheduled Strategy SNo Inputs to the model Elaborative Parameters as Inputs Description of each Input

Master of Science Thesis Ashish Dewan 44 Service Model

5-5 Service Simulation Block

Once all the inputs are loaded, the model combines all that information and simulate spare part replacement of each turbine over the chosen time period. For doing so, the simulation block is divided into smaller operational blocks which are presented in Figure 5-2. The description of each operational block is documented in following sections.

5-5-1 Operation: Weather Time Series

This operational block prepares a weather (wind-wave) time series that are represen- tative of the oﬀshore site. An hourly averaged time series of the same length as the date vector1 is built. The detailed explanation of the weather time series preparation is provided in Section 6-3.

5-5-2 Operation: Accessibility Vector

The accessibility vectors are required to determine the possibility for an access vessel to travel offshore to the WF. Corresponding to the weather thresholds provided as inputs for different access vessels, separate accessibility vectors of the same length of weather time series are built. Here, one signifies for favourable WF access and zero for the contrary. The accessibility achieved for each of the vessels used in the thesis is tabulated in Table 6-1.

5-5-3 Operation: Corrective Replacement

The estimation of corrective replacements is done in the same way as the spare part demand discussed in logistic model. Again, Homogeneous Poisson Process (HPP) is used to generate random failures throughout the project. The procedure for obtaining failure rates provided as an input to the operational block is discussed in Section 6-2-1. The time stamp of each failure is randomly and uniformly distributed between all the hours throughout the year. Entire list of corrective replacement events are recorded with the information of starting and ending time of each failure. Besides that, the failed WT, the spare code, spare maintenance type and spare ID are also tabulated. AppendixH shows one such example of a list. Note: The event list tabulated in the AppendixH also contains information of the associated downtimes (summation of MLDT, MWT, MTTR) for each failure.

5-5-4 Operation: Mean Time to Repair Modelling

The said operational block uses the mean and standard deviation values of MTTR inserted as inputs. The explanation for the initial MTTR processing using Lognormal distribution is described in Section 6-2-2. Thereafter, using random value generator, a

1Date vector is built with respect to the two speciﬁed inputs- Starting year of the project and Number of years of the project

Ashish Dewan Master of Science Thesis 5-5 Service Simulation Block 45

Figure 5-2: Flowchart of the Service Operation Block

Master of Science Thesis Ashish Dewan 46 Service Model

random absolute and appropriate value for each spare maintenance type and failure is generated.

5-5-5 Operation: Mean Logistic Delay Time & Access Delay Estimation

The delay mainly corresponds with the time of failure and before the service operation is started. For any corrective replacement, basically, there are two resources required- a replacement of a failed spare and an appropriate access vessel to undergo the repair. The non availability of either resources causes delay, and is counted as MLDT and Access Vessel Delay. The ﬂow of events under which the delay is caused is documented in AppendixA. From Figure A-1, the sequence of events are understood as follows:

• For each of the maintenance type, a different access vessel is required. Moreover, each vessel may or may not be available to the service team at the time of failure. For example, it is considered that for Class B replacement,a tender vessel or a work boat is sufficient and that is already available with the service team. On the other hand, for Class C and Class D repair, the team needs to wait for the right access vessel (Crane vessel or Jack-up barge), due to which there is a certain delay or lead time. Note that, in case of service vessel, there is always a deterministic lead time. The lead time chosen for the base cases is discussed in the next chapter. • At the same time, the spare part availability is also checked. If the spare part is present at depot, then the access vessel is ordered immediately in case of Class C and Class D maintenance type. Further, if the spare part needs to be shipped from CW, the lead time of bringing the spare is compared with the acquiring time of access vessel. Accordingly, a decision is made as to when the vessel is to be ordered. • Finally, in the extreme case of spare part not being present even at the CW, the exponential mean lead time associated with the procurement of spare is compared with the vessel lead time. Again, a wise decision of ordering time of vessel is made (only in Class C and Class D maintenance type). • It is noted that the spare part is checked and ordered as soon as any failure happens. Only the access vessel is ordered based on its lead time and spare pare availability. • Moreover, there is an overlapping between the MLDT and access vessel delay. The preference is always given to the logistic delay and then the difference between the cumulative and logistic delay is entered as access vessel delay. • Besides the above optimization of ordering time of vessel, there is another check made with the weather forecast in the coming days. With the current technology, it is possible to accurately predict the weather conditions for the next four days [42]. If the lead time of the access vessel is within four days and moreover it is observed that the next working day is not feasible for service operation, a further review is made for vessel ordering time. This is done to avoid the daily charges of the large access vessels.

Ashish Dewan Master of Science Thesis 5-5 Service Simulation Block 47

Only after the access vessel and spare part is procured, the service team maps out a plan for corrective replacement of the spare. This time stamp is termed as service start time.

5-5-6 Operation: Mean Waiting Time Evaluation

In this operational block, the service team makes the decision as to how to carry out the actual replacement operation. The service operation can be handled in diﬀerent ways based on the crew and the access strategy agreed and followed by the service operator. Some of the options a service operator could follow are listed below:

• Selection of the number of working hours. • Selection of service work boat, Crane vessel and Jack-up barge. • Provision of an oﬀshore accommodation. • Decision of staying overnight for larger replacement operations. • Minimum shift hours of good weather under which the crew plans to travel for repair operation.

These options are discussed in detail when the validation and implementation are done in Chapter 7. However, a basic working pattern followed in the execution of a replacement is explained in the order stated:

1. The service start time for a given replacement operation and the accessibility vectors are loaded. 2. Depending on the crew strategy chosen by the service operator, the service start time and the crew working shift hours are compared. If, for short-shift strategy, the service start time lies outside the crew working hours, work is delayed and counted as shift delay. 3. Then, for the working shift of the crew, the number of good weather hours are foreseen. This depends on the accessibility of the vessel used. If, for the working shift, the favourable hours are less than the minimum hours set as input (usually half the shift), the crew plans to call it a day and the downtime caused is termed as weather delay. However, if the accessibility to the WF is even equal to the minimum hours, the crew plans for the maintenance operation and returns back to shore before the weather condition goes bad. 4. Once the weather conditions are apt for maintenance work, the crew with the access vessel and spare part travels to the faulty turbine. The travelling time is considered in the overall downtime as travel delay. 5. After reaching the turbine, the crew and logistics are transferred from the access vessel to the turbine. The spare part is hoisted using either the permanent crane vessel or the external crane. This time is designated in the downtime as planning delay.

Master of Science Thesis Ashish Dewan 48 Service Model

6. For a short-shift strategy, the crew returns back to the base and the work on the turbine is resumed the next day. Then on, next morning the weather suitability is seen. In the case of full shift working patterns, the earlier service personnels returns back and another crew resume the replacement operation. 7. The crew continues to travel oﬀshore each day till the replacement of the spare is completed. Each day the weather and shift is checked and accounted for. The turbine remains unavailable till the replacement operation is accomplished. At the end, the downtime caused due to weather, shift, travel and planning is summed up. This delay is understood as MWT.

The explanation above is a basic and general outlook of the steps followed while performing a corrective replacement. The methodology will change depending on the strategy chosen by the operator. Some of the enhancements in the crew and access vessel strategies were listed above. Further discussion on such improvements will be mentioned later while discussing the test cases and validation of the model.

5-5-7 Operation: Scheduled Maintenance

This operation block calculates the downtime and the associated costs incurred due to scheduled maintenance of turbines. Specifically, it concerns Class E maintenance type. It is considered that the said maintenance operations are performed once in a year under low power production and preferably in good weather conditions. Based on user inputs, a specific month is selected for carrying out the maintenance. Also, the maintenance operations are done for a fixed number of hours for each turbine as agreed by the operator of the WF. The working procedure for undergoing scheduled maintenance is explained below. The procedure followed is specific to this model and may not depict the exact reality.

1. A separate work boat is hired for the annual maintenance of turbines. 2. The wind turbine is switched off just before the crew plans to carry out scheduled maintenance. Similar norms are considered for the off-shift hours, where the turbine is switched on. This implies that there is no travel downtime associated with the scheduled maintenance of turbines. 3. The crew always works according to a short-shift strategy. Hence, the crew returns to the shore after the day’s work. The off-shift period is counted as shift delay. 4. Every morning, the weather conditions are checked. If the travelling or the working conditions (wind and wave) do not adhere to the maximum threshold limits of access vessel, the crew has to postpone their maintenance operations. The non- working period due to extreme weather conditions is considered as weather delay. 5. When the crew reaches the wind turbine, there is a certain time spent in transferring the crew to the turbine, climbing the turbine and finally getting access to the turbine nacelle. This time is termed as planning delay and is deterministically assumed as one hour for every turbine operation. 6. It is considered that the maintenance of each turbine is done by a set of service personnels (defined as ‘crew’). Each crew works only on one turbine at a time.

Ashish Dewan Master of Science Thesis 5-6 Service Outputs 49

That is, only one turbine is switched oﬀ at a time. The crew will not move to the next turbine until the maintenance on the previous turbine is completed. 7. Finally, the overall downtime caused, the days it took to maintain the entire WF, the associated vessel and man- hour costs are computed.

Besides the scheduled maintenance performed by using the normal work boats, innovative access vessels can also be considered. One such strategy is discussed in Chapter 7.

5-6 Service Outputs

The service model primarily gives the information about the loss of time due to the corrective replacements and scheduled maintenance, the associated vessel and man- hour costs, the mobilisation of each access vessel. The values are obtained for the entire WF. Being a stochastic model, Monte Carlo simulations are used to converge the output for each of the parameter. The results obtained from service model are tabulated below:

Table 5-2: Outputs generated from the Service Model

S No Outputs of Service Model Elaborative Parameters as Outputs Description of each Output 1 Downtimes (Corrective Replacements) Based on the Class of Failure (hrs.) The downtime is estimated in two ways: depending on the class of maintenance type (B,C,D,E); Based on the delay caused (hrs.) or depending on the delay that causes the downtime (weather, travel, shift, planning, access, etc.) 2 Operational Availability (Overall) Availability of WF The average number of hours the wind farm is rendering a desired performance output. 3 Service Parameters (Corrective Replacements) No. of Mobilisations The number of trips undertaken by each of the access vessel. No. of Crew hours The number of hours the service personnel spend in performing corrective replacements. No. of Replacements The number of spares substituted after a failure occurs. Utilization of Contract vessels The number of days the larger vessels (Crane Vessel & Jack-Up Barge) are contracted. 4 Service Cost (Corrective Replacements) Man Hour Cost (Euro) The cost in lieu of the hours spent by the crew while performing maintenance operations. Access Vessel Cost (Euro) The cost incurred due to the number of trips performed by each of the access vessels. Total Service Costs (Euro) The overall expenditure obtained as a result of work carried out by service personnels and operational cost of service vessel 5 Scheduled Maintenance Downtime (hrs.) The number of hours the turbine is non-operational while performing regular maintenance. No. of days The number of days the crew takes to perform the scheduled maintenance. Man Hour Cost The cost in return of the eﬀort hours spent by the service personnel performing regular maintenance. Access Vessel Cost The cost acquired for hiring an access vessel, usually a work boat, for doing the scheduled maintenance. 6 Event List Spare Replacement events The complete database of the number of replacements and the associated spare code, spare type, downtime, etc. An example event list is tabulated in AppendixH

Master of Science Thesis Ashish Dewan 50 Service Model

Ashish Dewan Master of Science Thesis Chapter 6

Field Studies and Data Processing

The previous two chapters provided an insight for the design of a logistic and service model applicable to an offshore wind farm. This chapter focuses on identifying certain inputs which are necessary for modelling and generating results. Since the project is not aimed at any specific wind farm, parameters like failure rate or demand rate, MTTR, weather (wind-wave) conditions, spare part data and other logistics and service support organization information is difficult to attain in absolute terms. The data for above mentioned parameters is obtained from the sources available. The failure rate and MTTR data is computed from the raw Fraunhofer IWES WMEP database. The weather time series is prepared from two met masts- FINO1 and FINO2, located in North Sea and Baltic Sea respectively. The support organization information is referred from the open source service reports of Egmond aan Zee (OWEZ) and Nysted wind farms. Finally, the spare part data is prepared referring to the latest online reports and interviews. Moreover, for some parameters, processing of the data is required, for which the procedure followed is also explained with the given parameter documentation. The chapter is divided into five sections namely- Spare part data (Section 6-1), Wind turbine failure and MTTR data (Section 6-2), Wind-Wave time series preparation (Sec- tion 6-3), Logistics and Services data (Section 6-4) and Economic Data considerations (Section 6-5).

6-1 Spare Part Data

Spare part characteristics are of prime signiﬁcance when modelling a logistic model. Some of these characteristics are discussed in the sub-sections below:

6-1-1 Spare Part Selection

A modern 3 MW oﬀshore wind turbine consists of more or less the same sub-assembly arrangement. Modern turbines like Vestas V80, V90 and Siemens SWT 2.3, SWT 3.6 are

Master of Science Thesis Ashish Dewan 52 Field Studies and Data Processing

the preferable reference. These turbines are pitch controlled machines with nearly the same sub-components. Section 2-1-2 already has introduced the various sub-assemblies of a wind turbine. In this thesis research, individual spare part list is required, which meant a deeper insight than the sub-assembly level. For the same, component lists mentioned in [40] and [34] are referred. Moreover, the latter is a reference from Maintenance and Repair Report which was part of the 250 MW-WMEP project undertaken by Fraunhofer IWES. Also, as discussed later in Section 6-2, the failure rates are also derived from this database with reference to this list of components. After comparing the modern oﬀshore wind turbines used in base scenarios and the WMEP component list, a spare part list of critical components was prepared. In all, 29 spares are selected and henceforth used for modelling purposes. The stated Maintenance and Repair report (WMEP) and the spare list are documented in Appendix C-1. Each spare part is given a spare code as seen in the table of Appendix C-2.

6-1-2 Spare Part Price

Spare part pricing is quite difficult to obtain as no turbine manufacturer wishes to disclose these confidential information. National Renewable Energy Laboratory (NREL) of United States in one of their studies in 2005 estimated the price of a 3 MW offshore wind turbine [43]. Most of the costs are pointed out on a sub-assembly level. With an interest rate of 5% per annum on the given price and a time period of 8 years, the present values are projected in Euros (in 2013). Another source of attaining the price of the individual spare parts is through an online distributor of spare parts for leading turbine manufacturer in Netherlands [44]. Based on these sources, the spare part price are estimated. It is noted that for some spares, where the capital price is not available; a rough estimate is made comparing the other spare part costs. The list of the spares with their prices is tabulated in Appendix C-2.

6-1-3 Spare Part Type

As mentioned in the global assumptions (Section 4-1), the spares are categorized as repairable and discardable. The same can be deﬁned as:

• Line Replaceable Unit (LRU): LRUs are spares that are replaced in the system and then sent to a WS for repair, i.e. they are repairable items. LRUs are stored in a DEPOT or a STORE when they are not used by the system or when in repair. • Discardable unit (DU): DU is, like LRU, an item replaced directly in the system. The diﬀerence is a DU cannot be repaired, hence it is discarded.

The distinction between the LRU and DU is made with a feedback from a sales manager of a wind spare part company in Netherlands [45]. The list is tabulated in Appendix C-2.

Ashish Dewan Master of Science Thesis 6-2 Wind Turbine Failure and Downtime Data 53

6-1-4 Spare Part lead Times

Section 4-3-3 introduced the term, lead time of the spares with a distinction between external lead time and internal lead time. As stated there, the external lead time is considered stochastic (modelled according to exponential distribution) and the internal as deterministic. Since there is no information regarding the external lead times for the components, some sensible values have been chosen in consultation to the wind experts of Fraunhofer IWES. The mean values are listed in Appendix C-2. For internal lead time, the value is assumed to be constant and deterministic irrespective of the spare to be delivered.

6-2 Wind Turbine Failure and Downtime Data

In the period from 1989 to 2006, a large monitoring survey for onshore wind turbines (WTs) in Europe, the Scientiﬁc Measurement and Evaluation Programme (WMEP) had been managed by Fraunhofer IWES under the German publicly funded programme 250 MW Wind. Nearly 64,000 maintenance and repair reports were collected from over 1500 WTs [34]. It is the most detailed database used currently for the wind turbine reliability and availability. The raw data is conﬁdential and hence cannot be published in this report. Two of the most basic questions - ‘how often does a WT fail?’ and ‘Which WT downtimes are associated with which failure?’ are answered through the WMEP database. For this thesis, the same source is used. However, the database could not be directly used since it contained information of quite old onshore turbines. Hence relevant data processing was required to tune the failure (for estimating spare part demand) and downtime (for estimating the mean time to repair) records. The same are discussed in detail in Section 6-2-1 and Section 6-2-2 respectively.

6-2-1 Failure Data Processing

As mentioned above, estimates of failure rate or spare part demand and MTTR are made from Fraunhofer IWES WMEP database. The data treatment for predicting the failure rate and the spare part demand is discussed in this section and for the MTTR in the next subsection. As stated above, the database contained information of onshore turbines which were operating between the period from the year 1986 to 2006. This information could not be directly applied to this thesis. Hence turbines greater and equal to 1 MW are chosen for failure rate calculation. In all, 62 turbines are chosen from the database. Also, as seen in the Maintenance and Repair Report (in Appendix C-1), information for both corrective and scheduled maintenance can be derived separately. However, failure rate or demand rate corresponding to only corrective replacements are considered for the thesis work. Following are the steps involved in the processing of failure data:

Master of Science Thesis Ashish Dewan 54 Field Studies and Data Processing

1. The raw data is chosen for wind turbines greater and equal to 1 MW and the cases where replacement is performed is ﬁltered with each of the 29 spare parts. 2. In reliability theory, the failure rate is normally deﬁned as 1/MTBF. It can also be explained as:

No.ofF ailures (6-1) (UptimeOfT urbine) − (DowntimeOfT urbine)

3. By using the above formula; the no. of failures, the total running time and the downtime of the turbines are found for each spare. Finally the failure rate per year per turbine is computed for the corrective maintenance type. They are tabulated in Appendix C-3. 4. For some of the spares, there is no instance of failure recorded for the set of turbines chosen (under a limited period of operation). In those cases, a minimum value of 0.00001 is inserted. This is necessary to satisfy the global assumption of selection of critical spares. 5. Further, as stated before, the failure rate signify the demand of spare part. For corrective replacements, the spare part demand is modelled according to a constant rate following a Poison distribution. The use of these calculated failure or demand rates are for modelling the logistic and service model.

6-2-2 Mean Time to Repair (MTTR) Data Processing

Again, the processing is performed with the same set of onshore turbines greater and equal to 1 MW. Also, it is already mentioned that only mean downtime (MDT) values are available in the database. For the data treatment of the MTTR, primarily two eﬀects had to be negated. Firstly, the smaller values of small turbines (Onshore ∼ 1 MW) and secondly, the extremely higher values probably due to the logistic and other delays. Following are the steps involved in the treatment of the MTTR data:

1. The raw data is chosen for wind turbines greater and equal to 1 MW and where replacement is performed for each of the 29 spare parts. 2. The overall data for MTTR is quite less, hence both corrective and scheduled replacement values are combined for the analysis. 3. Since the values are that of onshore turbines which didn’t exclude the logistic and other delays as pointed out before, some of the values are filtered to negate the two effects mentioned in the introduction of this section. The values are set in line, which looked realistic for offshore Vestas and Siemens turbines. 4. By adopting the procedure mentioned in [33], the log-normal distribution parameters are estimated applying the Maximum Likelihood Estimator (MLE) technique. An example for the data treatment of one such spare part is explained in Appendix D-1.

Ashish Dewan Master of Science Thesis 6-3 Weather Time series Preparation 55

5. The mean (µ) and the standard deviation (σ) are provided as inputs and a stochastic input is used to determine the MTTR for a particular replacement of a component. The selected inputs for various spare parts are documented in Appendix D-2. 6. In some of the cases, the raw data is not available. In those cases, the values are approximated with close relation to other spare parts. E.g. power sensor, µ and σ are estimated from that of vibration switch, as both are sensors.

6-3 Weather Time series Preparation

Weather information is necessary to simulate the service model and determine the best strategy, which the operator should employ for his wind farm. For the thesis research primarily, meteorological information is collected from two sites representing the North Sea and Baltic Sea. The two met masts namely FINO 1 and FINO 2 are installed and monitored by the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) and Projektträger Jülich (PTJ), Germany. These sites are quite adjacent to the recently installed wind farms and also where future German wind farms are planned (Figure 6-1). For more information, the features for both the met masts are documented in Appendix F-1.

Figure 6-1: Location for three FINO Met Masts [www.ﬁno-oﬀshore.de/de/]

6-3-1 Weather Time Series Processing

The met masts installed are quite recent. Moreover, only limited information is accessi- ble for research purposes. In all, 3 years and 5 years of wind and wave data are provided by FINO1 and FINO 2 for this thesis research. According to the meteorological standards, the available wind speed data is recorded with a 10 minute timestamp and the corresponding wave height data with a 1 hour

Master of Science Thesis Ashish Dewan 56 Field Studies and Data Processing

timestamp. Throughout the series of retrieved data, irregularities are observed in terms of missing data, unrecorded values, etc. Hence, initially, necessary steps are performed in order to make a suitable weather (wind-wave) time series which could be passed as an input to the service model. An example with steps of treatment for preparing a FINO2 wind time series is documented in Appendix F-2. Further, both the wind and wave measurements are combined together in a single matrix with an hourly timestamp. Since the sets have diﬀerent time lengths, the extremes had to be trimmed so that WS and WH vectors start and end at the same time period. Note: For the veriﬁcation and implementation of the service model, North Sea wind farms are simulated with weather time series prepared from FINO 1. On the other hand, wind farms in Baltic Sea use FINO 2 weather time series.

6-3-2 Accessibility of Access Vessels

In general, it is believed that Baltic Sea is much more unpredictable than the neigh- bouring North Sea. This is quite evident from the time series prepared from the two met masts. For the four diﬀerent access vessels discussed in the thesis, the corresponding accessibility achieved for each of vessels under their working weather thresholds are tabulated in Table 6-1.

Table 6-1: Accessibility of Access Vessels w.r.t two Met Masts

Access Vessel Weather Threshold of vessel Accessibility Accessibility

(WST ,WHT ) (FINO 1) (FINO 2) Work Boat (working/travelling) 12 m/s, 1.5 m 55.69% 47.70% Crane Vessel (working) 10 m/s, 2.0 m 58.72% 51.89% Jack Up Barge (working) 10 m/s, 2.0 m 58.72% 51.89% Crane/Jack-up (travelling) 15 m/s, 2.0 m 83.83% 81.12% Mother Vessel (working/travelling) 15 m/s, 4.0 m 92.27% 92.12%

6-4 Logistics and Services Data

Oﬀshore wind farm (WF) is an assembly of number of wind turbines (WT) located oﬀ the coast. There are certain decisions made concerning the basic outlook of the logistic and service support organisation. For the model framework (as discussed in Section 4-2, information from already running and installed wind parks is considered. Though the original support organisation and the stock of spares is not available , the selection is made from the service reports of the existing WF. The two WFs are:

Nysted Wind Park is one of the largest oﬀshore wind farms and was commissioned in December 2003. It has been in operation for nearly 10 years. It is one of the two demonstration oﬀshore projects in Denmark. Necessary information concerning operation and maintenance of the farm is reported in the form of a technical report [27].

Ashish Dewan Master of Science Thesis 6-4 Logistics and Services Data 57

OWEZ offshore wind farm is the first offshore wind park in Netherlands. The WF was commissioned in January 2007 and is operational for more than 6 years. Since then, the project has been used as a research demonstration for further research of offshore wind farms in North Sea. There are two fully documented service reports available from year 2007 and 2009 in public [46][25].

6-4-1 Reference wind farms and Base Scenario Characterization

The Logistic and Service Model is developed from the two wind farms mentioned above. The following text highlights the basic support organisation of the two wind farms and the ﬁnally deciding upon input values for the O&M model.

6-4-1-1 Wind Farm Description

Table 6-2 lists the basic description of the two wind farms [7], [47].

Table 6-2: Wind Farm Description: Reference Wind Farms

Wind Farm Description Nysted WF OWEZ WF Location of the Farm Baltic Sea, Denmark North Sea, Netherlands Number of Turbines 72 36 Capacity of the farm 166 MW 108 MW Distance from Shore 10 km 10 km to 18 km

6-4-1-2 Support Organisation Description

The support organisation for the Nysted and OWEZ resembles that shown in Figure 4-1. However, in the case of OWEZ, there is no central workshop of the turbine operator (VESTAS) in Netherlands. So, whenever there is a spare required it has to be shipped from either Denmark or Germany. Following are the details of depot and central workshop of the two wind farms:

Nysted Wind Farm, Denmark (Baltic Sea) • Depot/Port: Small Ferry harbour- Gedser/ Port of Rodby Gedser is a town at the southern tip of the Danish island of Falster in the Guldborgsund Municipality in SjÃęlland region. It is the southernmost town in Denmark. It is an important small ferry harbour in Baltic Sea which is used for the service work for Nysted wind farm [7]. The port location and the wind farm are depicted in the ﬁgure of Appendix E-1. • Central Workshop: Brande, Denmark Brande in Denmark is the main hub for Siemens wind turbine functions. Brande is situated in the middle of Jutland, Denmark. The production facility for SWT-2.3 is situated here and also the main storage facility for spare parts. Most spare parts ordered for the wind farms come from Brande. This

Master of Science Thesis Ashish Dewan 58 Field Studies and Data Processing

is also where faulty items (LRUs) are sent for repair. The distance between the CW and the depot is depicted in Appendix E-2. OWEZ Wind Farm, Netherlands (North Sea) • Depot/Port: Ijmuiden (near Wijk aan Zee) IJmuiden (Netherlands) belongs to the municipality of Velsen and is the main port for the O&M of the two offshore wind farms of NL. The port location and the wind farm are depicted in the figure of Appendix E-1. • Central Workshop The head office of Vestas Offshore is in Randers, Denmark with a shared facility with head office Vestas Wind Systems A/S. It is responsible for the Vestas spare parts and repair support for all of the Vestas operational wind farms including the offshore ones [48]. The distance between the CW and the depot is depicted in Appendix E-2.

6-4-1-3 Lead Time (from Central Workshop to Depot)

As explained before in Section 6-1-4, the lead time for a spare to be received at the depot from the central workshop (in case of backorder) is deterministic. Following are the estimated times for the diﬀerent scenarios:

For Nysted offshore wind farm The time between the Siemens factory in Brande and the depot in Port of Gedser is a bit difficult to estimate. This is the time for obtaining a new spare and also when an item (LRU) is repaired in Brande and thereafter received in the depot. Driving between the sites takes about 3 − 6 hours and to coordinate the entire process, the transportation can take an entire working day. With the spare not being ready for use till the next day, the total MWT is estimated to be 24 hrs. For OWEZ offshore wind farm The time between the Vestas factory in Randers and the depot is approximated to around 8 hours (by road). Since the spare is to be shipped from another country, more logistic delay time is assumed while obtaining a spare at the depot. The estimated time is 2 days (48 hours). The sensitivity analysis for the lead time of a spare part is discussed further in Section 7-1-1.

6-4-1-4 Crew Members

The Services and Logistics require support staff to implement the operations. For logistics, there are 2 men employed working 7 days a week. For the service part of the operations, the working crew are contractually available for seven days a week and follow a short-shift strategy or full-day strategy (24 hrs.). The crew working definition is the same for all the scenarios. The repair crew’s base salary and other expenses related to training, insurance, equipment and bonus are significantly higher in the case of offshore technicians, when compared to the normal onshore conditions, thus the number of crew is very important parameter to optimize.

Ashish Dewan Master of Science Thesis 6-4 Logistics and Services Data 59

The number of crews should be directly proportional to the size of the oﬀshore wind farm. Hence for the base scenarios, a total of 12 people (services) are assumed. This is ﬁnalised based on information available from the 2 wind farms into consideration [27] [46]. For a corrective replacement, a crew of 3-6 people sail down for the change of the faulty spare, whereas for the regular scheduled maintenance, a crew of 6 people perform the maintenance activities on a turbine.

6-4-1-5 Access Vessel

The work boats that are employed for the service maintenance (for reference WFs) are Katrine or FOB Lady [27][46][7]. These tender vessels can even sail with WHs of 1.5 metres. The maximum capacity for both the tender vessels is 12 persons. If the crew working strategy is short-shift, then it is assumed that there is only one tender vessel in possession with service team for either of the base scenarios. On the other hand, with a full-shift crew strategy, two tender vessels are required. Use of tender vessel or boat as an access vessel is necessary for all the classes of repair (Class A, B, C, D operations). There is no lead time associated with the boat as it is always in possession of the service team. However, for Class C operations, in addition to the boat, a separate build up crane vessel is needed which hoists the crane over the turbine nacelle. They are contractually bought when required to perform a repair operation and associates 1 day of lead time [40]. Further, for Class D operations, where in large components are required to be replaced; a Jack-up barge is used. With regards to the base cases, a barge named Wind from DDB Jack-up barge services is used [47]. Ownership or Call-oﬀ strategy is used for this purpose and a lead time of seven or twenty-one days is associated with bringing in the crane vessel and jack-up barge. The various vessels used in research are documented with pictures in Appendix E-3.

6-4-1-6 Regular Scheduled Maintenance

The parameters associated with the scheduled maintenance service is derived from the observation from service reports of two referred wind farms- OWEZ and Nysted wind farms. Though both the wind farms have individual strategy, useful and appropriate conclusions are derived to formulate a common strategy. Following are the details of the scheduled activities [27][46]:

Maintenance interval: Once an Year The scheduled maintenance is an annual activity performed for improving the turbine reliability. It is with reference to the deterministic calculation of cost incurred doing regular maintenance activities. The activities include the basic fixing of screws and greasing of components, refilling of the gearbox oil tank and cleaning generator filter, etc. Maintenance time (the number of hours each system is down): 9 hrs. It is the amount of time, the crew needs to undertake maintenance activities on the

Master of Science Thesis Ashish Dewan 60 Field Studies and Data Processing

wind turbines. For the base scenarios, a crew of 6 service personnels are required. It means it takes approximately a working day to complete the maintenance activities on one turbine. Cost for each preventive task The total cost is the summation of access vessel (per mobilisation), crew (man- hours) and the small components like nuts, grease, filters, etc. The cost of last components are difficult to estimate, hence a fixed cost of the same is assumed. Also, for all the cases, it is decided that a separate tender vessel is hired. This does not obstruct the regular replacement operations. The scheduled maintenance cost for small components for the base scenario is considered 3000 EURO per wind farm. The lost revenue due to the downtime of the turbine is calculated separately.

6-5 Economic Data Considerations

• Item Consumption Costs is the same as discussed in Section 6-1-2 . It is enlisted in Appendix C-2. No discounts on the item are assumed for larger order size. • Holding Cost/Storing Cost is expressed as proportional to the value of a spare (EURO per item). In this thesis, it is considered 20% of the item consumption or the capital cost. The percentage is the same for all the spares. • Reorder Costs are the fixed cost applicable only for the central workshop where there is an order quantity. 400 Euro per order is considered for the thesis. • Backorder Cost/Shortage Cost is again considered in proportion to value of a spare (EURO per item). In this thesis, it is reflected as 20 % of the spare price. • Man-Hour Costs is the hourly wage per crew member. It is set as 70 EURO for this thesis [40]. For night shifts (in the case of full-shift working strategy), the hourly wage is set as 105 EURO for this thesis. • Tender Vessel Costs:The daily cost for hiring a normal tender vessel or a work boat is assumed to be 2000 EURO for this thesis research. • Crane Vessel Costs: The crane vessel is normally rented on a daily basis. It includes a fixed mobilisation cost of 45K EURO, demobilisation cost of 45K EURO cost for each event and an additional variable cost of 20K EURO per day. • Jack-up barge Costs: Again, the Jack-up is rented on an event basis, where in addition to the mobilisation cost of 57K EURO and demobilisation cost of 45K EURO, there is a variable cost of 16K EURO per day [40].

In this chapter, the values of all the relevant and necessary parametersspare part, logistics and service, failure and economic are explained and established. In the next chapter, this information will be used in the right perspective as an input for the model veriﬁcation and implementation.

Ashish Dewan Master of Science Thesis Chapter 7

Model Veriﬁcation and Analysis

The previous chapter discussed the field studies and the corresponding values chosen for the Logistic and Service model. This chapter evaluates the response of the O&M model to different input parameters. For the same, sensitivity analysis (Section 7- 1), comparison studies (Section 7-2) and extreme scenarios testing (Section 7-3) are performed. The results and outputs are obtained with respect to the entire WF and its total operational lifetime. Later, model is simulated for some planned WFs to illustrate a specific model usability and implementation. (Section 7-4). The simulation working procedure followed here is as discussed in Chapter 4 and Chapter 5. The input values are applied from the field studies documented in Chapter 6. Table 7- 1 summarizes the input values considered for the model verification in form of sensitivity analysis, comparison studies and extreme value testing. The weather threshold values are corresponding to the operative limits of work boat, crane vessel and jack up barge. The same can be seen in the GUI documented in AppendixB. Besides the tabulated inputs, the other required parameters namely: failure rate, mean time to repair, spare part information are the same as mentioned in Chapter 6.

7-1 Sensitivity Analysis

Sensitivity analysis helps in testing the robustness of a model. At the same time, it increases the understanding of input-output variables relationship, thus enabling the user to apply the model for different scenarios. This section discusses five sensitivity tests, where the objective is to show the difference it can make in the overall availability and service costs by improving or deteriorating logistic or service efficiency. Besides the cases stated in this section, the sensitivity analysis for distance to shore, number of turbines and month of scheduled maintenance has also been discussed later in Section 7-2-4.

Master of Science Thesis Ashish Dewan 62 Model Veriﬁcation and Analysis

Table 7-1: Input Parameters for Validation of the model

S No. Inputs to the model Elaborative Parameters as Inputs Input Values 1 WF Information Starting year of Project 2013 Operational lifetime of WF 20 years Number of Turbines in WF 36 Number of Runs 30 Manipulation Parameters 5 2 Crew Strategy Shift Patterns Short- Shift Pattern Shift Starting Hours 06:00 Shift Ending Hours 18:00 Minimum Working Period 6 hrs. Eﬃcient Working Patterns No Overnight Staying Provision No 3 Transport Strategy Access Vessel Speed 18 knots Mean Distance from Port to WF 30 km Lead Time Spare Part from CW to Depot 24 hrs. Lead Time Access Vessel 24 hrs.(Crane), 168 hrs.(Jack-up) Availability of Oﬀshore Accommodation No 4 Weather Thresholds Wind Speed Thresholds 12 m/s, 10 m/s, 10 m/s, 15 m/s Wave Height Thresholds 1.5 m, 2 m, 2 m, 2.5m 5 Scheduled Strategy Scheduled Maintenance hours per turbine 9 Starting Month of maintenance June Implementation of Special Case No Number of Crew on Access Vessel 12

Ashish Dewan Master of Science Thesis 7-1 Sensitivity Analysis 63

7-1-1 Logistic Model: Sensitivity Analysis for Lead time to Local or Central Warehouse

The efficiency of either of the inventory at depot or CW depends on how fast the spare part can be delivered when the demand occurs. This definitely affects the backorder cost and hence the total inventory cost. Such a test case was simulated with the lead time varying from 24 hours to many days. The response of the logistic model is documented in Figure 7-1.

10000

9000

8000

7000

6000

5000

4000

3000

Lifetime Inventory Cost [Euro] 2000

1000

0 0 50 100 150 200 250 300 350 400 Lead Time for a spare part from CW [hours]

Figure 7-1: Sensitivity Analysis for change in Lead Time of the spare part at depot

The trend shows that the lead time for obtaining a spare from the CW at the depot affects the lifetime inventory cost. This signifies the importance of having a central workshop in the close proximity to the wind farm’s depot. Moreover, for countries like Netherlands, where there are no central workshop for leading turbine manufacturers, the stock levels at the depot are always different from the one in Denmark or Germany in order to compensate for the extra lead time.

7-1-2 Service Model: Sensitivity Analysis for selection of Access Vessels

There are primarily three access vessels discussed until now. The contract based large vessels are less available in the market and there is not much choice between them. Moreover, each of these large vessels are constructed with rather the same speciﬁcations. The scope of improvement lies in the selection of normal work boats used for the Class A, B, E maintenance operations. Two diﬀerent sensitivity parameters are altered from the initial input parameters, namely, access vessel speed (BS) and wind speed threshold (WST ). Note that for testing both the cases, it is considered that all the spares are replaced by Class B maintenance operation. The results to both the said parameters are shown in Figure 7-2 and 7-3 respectively. From Figure 7-2, one can observe that, more the speed of the access vessel, faster the reaction time to the WF. This reduces the overall downtime and improve the overall availability of the WF. Figure 7-2 clearly shows the inverse relation between the travel delay and availability. Also, the impact on the availability is higher as the distance from shore increases and it is better to choose boats with higher speed.

Master of Science Thesis Ashish Dewan 64 Model Veriﬁcation and Analysis

96 400

94 200 Availability [%] Travel Delay [hrs]

92 0 15 20 25 30 35 40 Access Vessel Speed [knots]

94 1600 1400 92 1200 1000 90 800 600 88 400 Availability [%] Travel Delay [hrs] 200 86 0 15 20 25 30 35 40 Access Vessel Speed [knots]

Figure 7-2: Sensitivity Analysis for change in Access Vessel Speed at (i) 50 km and (ii) 100 km distance from shore

1 1

0.9

0.8

0.7

0.6

0.5 0.5

Availability [%] 0.4 Accessibility [%]

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0.2

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0 0 2 4 6 8 10 12 14 16 18 20 Wind Speed Threshold for Access Vessel [m/s]

Figure 7-3: Sensitivity Analysis for change in Wind Speed Threshold

Further, another feature of the access vessel which needs to be improved is the feasibility of its operation under adverse weather conditions. From Figure 7-3 it is evident that the operational availability of the WF gets better along with the accessibility to the WF.

Ashish Dewan Master of Science Thesis 7-1 Sensitivity Analysis 65

7-1-3 Service Model: Sensitivity Analysis for Lead time of Contract-based Vessels

Large vessels such as Crane vessel and Jack-up barge are not always in possession of the WF operator. At the time of Class C or Class D replacement, these vessels have to be bought on contract for a certain period of time. As mentioned before, the service cannot be started until both the spare parts and access vessel are available to the service team for replacing the spare. The lead time of the vessel depends on the contract that is signed between the vessel provider and the operator of the WF. This can vary from anything between one day to several weeks. The following graph shows the response to the lead time of the contract vessel.

x 104 100 2

95 1.5

90 1 Availability [%] Access Vessel Delay [hrs]

85 0.5

80 0 0 100 200 300 400 500 600 Lead Time for a Contract Vessel [hrs]

Figure 7-4: Sensitivity Analysis for change in Lead Time of a Contract Vessel

As expected, it is observed that the access vessel delay increases drastically directly influencing the overall operational availability of the WF. It should be noted that with better contracts, there is always some additional cost. Hence, a trade off is required between the money lost due to loss of production and the extra cost in bringing the vessel earlier. For clear affect of access delay, the spare part is considered to be always available, that is MLDT is zero.

7-1-4 Service Model: Sensitivity Analysis for Crew Strategy Working patterns

The working hours of the crew again depends on the labour contracts that are signed between the crew members and the operator. It should be clear that for the entire length of year, the working hours remain the same. Additionally, the operator checks the minimum hours of good weather before the crew decides to travel oﬀshore. Short- shift strategy is considered with the starting time of the shift as 6:00 hrs. in both the cases. The sensitivity analysis to these parameters are performed and the output graphs are shown in Figure 7-5 and Figure 7-6 respectively.

Master of Science Thesis Ashish Dewan 66 Model Veriﬁcation and Analysis

100 14000

12000

10000

96 8000

6000

Availability [%] 94 Total Shift Delay [hrs]

4000

2000

90 0 12 14 16 18 20 22 24 Shift Ending Time [hrs]

Figure 7-5: Sensitivity Analysis for change in Shift Working Hours

6000 90

5500 85 Total Downtime [hrs] 5000 80 Number of Vistis to the Farm

4500 75 6 7 8 9 10 11 12 Minimum possible working hours [hrs]

Figure 7-6: Sensitivity Analysis for change in Minimum feasible Hours

In the case of shift working hours, as anticipated, with increase in working hours, the shift delay decreases with increase in availability. Also, it is observed that using the normal short-shift strategy with a total shift of twelve hours, adequate availability can be obtained with limited shift delay. Another aspect is the health and safety guide- lines for oﬀshore maintenance work which limits the crew to work for extra hours [49]. Further, the second case is a decision which lays more in the hands of O&M management. Though, the number of trips to the farm decreases substantially with increase in minimum working hours saving money on the service costs, but the weather delay and hence the total downtime increases. Moreover, for larger contract based vessels, it is not a very good decision to always delay the working, as they are bought on a high

Ashish Dewan Master of Science Thesis 7-2 Comparison Study of Logistic & Service Strategies 67

cost of daily basis. Although with increasing distance to farm, the service team has to increase the working weather window. This signifies that a trade off is required for a best suitable option to be selected. Later, in Section 7-2-3 different and appropriate crew strategies are discussed in detail for a comparison study. The sensitivity tests discussed above, builds one to one relationship between the input and output parameters. The next section introduces effective and appropriate O&M strategies which is designed, keeping in perspective the future larger and farther offshore wind farms.

7-2 Comparison Study of Logistic & Service Strategies

The section does a comparison study by employing different O&M strategies. The section will enable the operator of the WF to choose and understand different possible logistic, access and crew strategies. Again, the outputs are analysed for the entire WF and its lifetime. Moreover, there are certain necessary changes in the input parameters that are done while implementing the different comparative studies. The changes will be highlighted for each of the cases discussed below.

7-2-1 Logistic Model: (R,Q) policy vs. (S − 1,S) policy for Central Work- shop

The logistic model explained in Chapter 4 depicts a multi-echelon system with just one wind farm. This implied that the demand at the depot and the CW is same. In the comparison study, there are more than one wind farms handled by the same CW. In such a situation, the implementation of base stock or the (S − 1,S) policy might not be relevant at the CW. The other ordering policy ((R,Q) policy) explained in Section 3-4 is compared with the base stock policy. The comparison is done at the CW alone for a single spare. Following are some of the modelling characteristics necessary to be understood. They are listed below:

• Employing base stock policy, there are two CW separately supplying spare parts to each of the depots. • For (R,Q) policy, the demand at one CW from two wind farms is compound Poisson. Further, the two WF are considered to be identical in terms of size, turbines and demand of spare parts. The multi-echelon system in this case looks like the one shown in Figure 3-2. • The value of Q, that is the ordering amount is solved by the classic EOQ formula. The formula is as follows: s 2 ∗ OrderingCost ∗ DemandRate Q = (7-1) HoldingCost

• Single-item approach is still followed as the demand is still lower and involves slow moving critical spare parts.

Master of Science Thesis Ashish Dewan 68 Model Veriﬁcation and Analysis

• The holding and backorder cost are 25 % of the spare part price.

The comparison is done considering just the demand rate, lead time of a single spare, i.e. a generator. The total inventory cost for the two cases are shown in Figure 7-7 and 7-8.

1700

1600

1500

1400

1300

1200

1100

1000

Lifetime Inventory Cost [Euro] 900

800

700 0 1 2 3 4 5 6 Ordering upto level at each of the Central Workshop

Figure 7-7: Inventory Cost following a (S − 1,S) policy with two separate Central Workshops

2200

2000

1800

1600

1400

1200 Lifetime Inventory Cost [Euro]

1000

800 −1 0 1 2 3 4 5 6 Reorder level at a single Central Workshop

Figure 7-8: Inventory Cost following a (R,Q) policy with a single Central Workshop

The results following the two diﬀerent policies are understood as follows:

• The optimized reordering parameter (R) at the central workshop is two. This signiﬁes that whenever the number of generators reach the value of two, there is an order passed with order quantity Q. • The optimized order up-to level or the base stock level at the independent central workshops is three. This means that whenever the value of S reaches three, another generator is added to the stock. Also the inventory cost calculated is the summation for both the central workshops. • Though the optimized cost following a base stock policy is less than the batch ordering policy, the calculations ignore the setting up cost of the inventory. This value will deﬁnitely make the entire support cost more in the case of independent base stock policies.

Ashish Dewan Master of Science Thesis 7-2 Comparison Study of Logistic & Service Strategies 69

• With turbine manufacturers and owners building more wind farms in the close proximity to their central workshop, having a single central storage place following a batch ordering policy is advantageous.

7-2-2 Service Model: Oﬀshore Accommodation Possibility

With offshore wind farms going farther from the coast, the necessity of an offshore accommodation could arise. Horns Rev-2, the recently commissioned WF in Denmark has initiated such an installation [47]. The offshore platform has a capacity of housing twenty-four crew personnels besides storing small spare parts (Figure 7-9). Building of such an installation can be quite expensive and its justification should be validated. This test case compares the case of WF with and without an offshore platform. Also, the effect with increasing distance is explored.

Figure 7-9: Oﬀshore Accommodation installed at Horns Rev-2

7-2-2-1 Working Simulation

• In the case of no offshore accommodation , the crew follows the working and access strategy as described in the Chapter5 and with inputs specified in Table 7-1. • In the case of an offshore platform with crew following a short-shift strategy, the crew need not find more working hours than the minimum hours it takes to travel and reach the offshore platform. The crew does not have to go back considering that they have an offshore facility to stay overnight. • Also, for large contract vessels, the weather thresholds for sailing to the WF and crane operation are different. In most cases, the sailing thresholds are higher and this works in favour of offshore accommodation. • Primarily, the service cost and the generated revenue is compared. If the difference between the two can overcome the investment cost and the maintenance of the offshore platform, the implementation of such a structure would be advantageous. • The comparative studies are performed for three different distances to farm: 30 km, 60 km, 100 km.

Also, there are certain assumptions made in order to make the comparisons. They are as follows:

Master of Science Thesis Ashish Dewan 70 Model Veriﬁcation and Analysis

• Considering that the crew stays offshore even during the non-shift hours, the salary slab is higher as compared to the case where there is no offshore accommodation. It is supposed that the crew are paid 1.25 times compared to the other case. • The crane vessel’s speed has been increased by 2 knots from the initial value set in Table 7-1. This is done in order to make it possible for the crew to travel offshore in case the distance from the shore is 100 km. • For the calculation of generated revenue of the WF over the lifetime, a capacity factor and the electricity tariff are considered as 0.3 and 0.12 Euro per kWh respectively.

Table 7-2: Comparison with or without an oﬀshore accommodation based on the lifetime analysis of a WF

Parameters Oﬀshore Accomm. No Oﬀshore Accomm.

Availability (%) 86.71 83.14

Downtime (Total hours) 2.56E+04 3.31E+04

Nmobilisations Boat 55.57 447.53 Nmobilisations Crane 23.33 23.43 Nmobilisations Jackup 36.53 37.20

Access Vessel Costs (Euro) 1.79E+07 2.30E+07 Man Hour Costs (Euro) 5.07E+06 1.14E+06 Total Service Costs (Euro) 2.30E+07 2.41E+07

Generated Revenue (Euro) 5.91E+08 5.66E+08

Net Revenue (Euro) 5.68E+08 5.42E+08

Table 7-2 summarizes the output for a standard oﬀshore WF located 30 km from shore. Following are the points which are noted:

• As expected, there is an increase in the availability of the WF when a provision of an offshore platform is provided. • With an offshore platform, the mobilisations of boat decreases drastically as the crew need not return. Also for larger replacements, no extra work boat is expected to travel. This definitely helps in reducing the access vessel costs. • The increased availability impacts the net revenue generated. Hence, the net revenue achieved for the WF over the lifetime is higher with an offshore accommodation.

Furthermore, as mentioned before, the analysis is done for WF at different distances to the shore. Table 7-3 sums up the effect of installing an offshore platform with increasing distance from shore. It is clearly evident that with increasing distance of WF from shore,

Ashish Dewan Master of Science Thesis 7-2 Comparison Study of Logistic & Service Strategies 71

the installation of an oﬀshore facility would be advantageous to the overall cost of the project.

Table 7-3: Diﬀerence in Net Revenue for a WF with and without oﬀshore accommodation

Distance from shore (km) Diﬀerence in Net Revenue (Million Euro) 30 25.5 60 45.9 100 78.6

7-2-3 Service Model: Crew Shift Working Strategies

In Section 7-1-4, two sensitivity tests are performed, wherein, the effect of changing the shift working hours and minimum hours of workable weather are changed. This section specifically compares different crew strategies, which could be part of the contract signed between the personnels and the operator of the WF. Firstly, three different working patterns are compared. They are: (a) Short shift strategy with twelve hours of shift time, (b) Full shift strategy with twenty-four hours of shift time and (c) Efficient shift patterns with twelve or twenty-four hours of shift time depending on the failure and maintenance type. In the case of twenty-four hours of working two set of crews work for twelve hours each. The working strategy for this comparison is listed below: • For the short-shift strategy, the crew follows the access strategy as described in Chapter 5. Rest of the inputs remain the same as mentioned in table 7-1. • For the full-shift strategy, the two set of crew work for twelve hours each. Also, two work boats are needed in this case as the crew needs to change after each twelve hours. Though in the case of large replacements, only one work boat is used with the contract based vessel. • For efficient-shift strategy, the crew follows short-shift strategy (twelve hours) for Class B replacements, while, for Class C and Class D full- shift strategy (twenty- four hours) is used. It possesses the advantage of both the other mentioned strategies. By performing Class B replacements by short-shift strategy, there is no need for an extra work boat to be available with the service team. Also, the larger replacements are performed following full-shift strategy. This helps in reducing the renting time of the large and expensive vessels (used for Class C and Class D replacement). Again, as done in the previous comparison test (Section 7-2-2), the results are compared in terms of net revenue for the entire WF over its lifetime. The outputs in each of the case are tabulated in Table 7-4. The Table 7-4 clearly indicates that the net revenue earned is maximum in the case of full shift strategy. But if we just compare the total service costs, the efficient shift strategy is the most suitable due to its combined features of both the short and full shift strategy. Such a shift strategy definitely improves the working pattern of the crew. The operator can choose which strategy suits best to the operational capacity of the WF and the working efficiency of its crew members.

Master of Science Thesis Ashish Dewan 72 Model Veriﬁcation and Analysis

Table 7-4: Comparison of three diﬀerent Crew Strategies

Parameters Short Shift Strategy Eﬃcient Shift Strategy Full Shift Strategy (12 hr strategy) (12/24 hr strategy) (24 hr strategy) Availability (%) 83.42 86.11 88.04

Downtime(Total Hours) 3.24E+04 2.70E+04 2.26E+04

Nmobilisations Boat 400.63 410.03 854.71 Nmobilisations Crane 23.57 24.27 24.87 Nmobilisations Jackup 35.27 36.10 37.00

Access Vessel Costs (Euro) 2.24E+07 1.80E+07 1.92E+07 Man Hour Costs (Euro) 1.06E+06 9.61E+05 1.54E+06 Total Service Costs (Euro) 2.35E+07 1.89E+07 2.08E+07

Generated Revenue (Euro) 5.68E+08 5.87E+08 6.00E+08

Net Revenue (Euro) 5.45E+08 5.68E+08 5.79E+08

Further, Section 7-2-2 discussed the advantage of building an offshore accommodation. However, there is a certain alternative with the operator to choose for. Large vessels like Crane and Jack-up barge can accommodate 30-40 service personnels with ease [47]. Such an option is a decision made by the operator and followed, based on the contract signed between him and the contractor of the vessel. With the wind farms closer to the farm, the operator opts for its crew to return back to the shore. But with WF going further from the coast a possibility of staying overnight in these large vessels will be relevant for better performance of the WF. The scenario is simulated in the O&M model and results for a WF, with and without the provision of staying overnight, is tabulated in Table 7-5. Similar results can also be obtained corresponding to the crew following a full shift or an efficient shift working strategy. Moreover, the effect of its implementation at different distances can also be obtained as is done in Section 7-2-2. Table 7-5 clearly shows the difference in the revenue earned. An approximate of 21 million Euro can be earned over the lifetime if we implement the said provision in the form of a clause in the contract signed between the operator and contractor of access vessel.

7-2-4 Service Model: Access Vessel Strategies performing Schedule Main- tenance

Offshore wind energy industry is growing enormously with Germany and UK having plans to implement bigger offshore wind farms further from the shore. The need to improve the service practices within the offshore wind energy industry arises automatically. Keeping that in hindsight, there are a lot of vessel manufactures which are constantly

Ashish Dewan Master of Science Thesis 7-2 Comparison Study of Logistic & Service Strategies 73

Table 7-5: Comparison for choosing overnight in large contract based vessels

Parameters Without Overnight Staying Option With Overnight Staying Option

Availability (%) 82.4516 85.2099

Downtime (Total Hours) 3.48E+04 2.85E+04

Nmobilisations Boat 417.3667 173.2333 Nmobilisations Crane 23.9667 25.6 Nmobilisations Jackup 36.9667 37.6

AccessVessel Costs (Euro) 2.36E+07 1.96E+07 Man Hour Costs (Euro) 1.02E+06 2.97E+06 Total Service Cost (Euro) 2.46E+07 2.25E+07

Generated Revenue (Euro) 5.62E+08 5.80E+08

Net Revenue (Euro) 5.37E+08 5.58E+08

pressing for innovation and improvement in their design, facility, technology and cost optimization. Regular work boats as described in Section 6-4-1-5 will be inefficient and hard to employ for future offshore wind farms. Keeping that in perspective, one of the offshore marine company- Sea Energy Marine in 2011 proposed a design of a massive vessel, 76 m in length, which is dedicated for the operation and maintenance of offshore wind farms [6]. Some of the better features of such mother vessels include the provision of a motion compensating gangway, operations capability up to 4m Hs, facility of multiple access systems including daughter crafts (small work boats), a helideck and competency of operating on-site for extended periods; twenty four hours a day, seven days a week. Also, it can store various high-end tools, accommodate 60 personnel, has well equipped medical centre and much more. A sketch of the proposed mother vessel is shown in Figure 7-10. The facilities provided by such a mother vessel looks appealing, but every such significant innovation demands huge amount of investment and the challenge here is to convince the operator or the owner of the wind farm to invest in these advance ships as soon as possible. Annual hiring of these mother vessels is quite difficult in current financial state of wind industry. Hence, to justify the feasibility of renting such gigantic ships for operation and maintenance, a simulation is set up with an arbitrary WF undergoing an annual scheduled maintenance. Later, three near future offshore WF are chosen to compare and decide the best working access strategy. The reason for implementing the test case with annual scheduled maintenance is the justification of the vessel’s high cost, where it could be hired for a decent period of time without it being ideal. Four different cases are modelled in order to check the different access vessel strategies and the feasibility of employing mother vessel for future operations. For the simplest

Master of Science Thesis Ashish Dewan 74 Model Veriﬁcation and Analysis

Figure 7-10: Mother Vessel Sketch [6]

of the existing strategy practised for annual maintenance, the use of work boats (e.g. Katrine, Windcat) is considered (Case 1). The mother vessel is used in three diﬀerent ways: (a) Mother vessel alone for the maintenance purposes (Case 2); (b) Mother Vessel as an oﬀshore accommodation and using the daughter work boats for the maintenance purposes (Case 3); (c) Mother vessel and daughter work boats both being used for the maintenance (Case 4). Each of the four cases has certain constraints that of weather, the sitting capacity, gangway facility, etc. The four cases are summarized in Table 7-6.

7-2-4-1 Working Strategy for Simulation

Scheduled maintenance of oﬀshore WF is usually done once a year under low power production and preferable weather conditions. Following points explain the working of the simulation procedure set up for comparing diﬀerent cases (access strategies).

• Two work boats are required to be hired for the maintenance of turbines (case 1). In the latter cases, the mother vessel alone is hired for the period of schedule maintenance of turbines. • The crew works according to a short shift strategy (12 hour shift patterns). Hence the crew returns to the shore (in case 1) and to the mother vessel (in case 2, 3, and 4). The oﬀ-shift period was counted as shift delay. • Every morning, the weather conditions are checked. If the travelling or the working conditions (wind and wave) do not adhere to the maximum threshold limits of access vessel, the crew has to postpone their maintenance operations. The non- working period due to extreme weather conditions is considered as weather delay. • When the crew reaches the wind turbine, there is a certain amount of time spent in transferring the crew to the turbine, climbing the turbine and ﬁnally getting access to the turbine nacelle. This time is termed as planning delay and is deterministically assumed as one hour for every turbine operation besides every morning.

Ashish Dewan Master of Science Thesis 7-2 Comparison Study of Logistic & Service Strategies 75

Table 7-6: Summary of input parameters for the comparison of access strategy

Case 4: Case 3: Use of Case 2: Use of Case 1 : both Use of daughter Comparison Use of Mother Mother work Parameters Work vessel and vessel boats on boats daughter (alone) Mother work Vessel boats Provision to NO YES YES YES stay oﬀshore Gangway NO YES NO YES Provision 12 m/sec M.V:15 m/ Wind Speed (travelling 15 m/s 12 m/s s (working) Threshold and (working) (working) Boat:12 m/ working) s (working) 1.5m Wave Height Hs(travelling 4m 1.5m M.V: 4m Threshold and Hs(working) Hs(working) Hs(working) working) Boat:1.5m Hs(working) 12 service 6 service 12 service 18 service Crew Strength personnels personnels personnels personnels Additional Access Boats required 1 1 1 2 after 20 day limit Max. sitting capacity of 12 60 60 60 access vessel

Master of Science Thesis Ashish Dewan 76 Model Veriﬁcation and Analysis

• It is considered that the crew works only on one turbine at a time. That is, if two sets of crew travel simultaneously (e.g. in case 1); two turbines are maintained. This also means that only two turbines are switched OFF at a time. • The wind turbine is switched OFF just before the crew planned to schedule maintain it. Similar norms are followed for the oﬀ-shift hours, where the turbine is switched ON again.

Besides the working strategy listed above, there are certain assumptions which are made in order to make this comparison:

• The work distribution between the two work boats in case 1 and case 3 is equal. In case 4, the distribution of work is made from the simulation results of case 2 and case 3. It was noted that by employing mother vessel alone, it took approximately 80 days to schedule maintain the WF. In case 3, the overall time taken was 90 days. Hence, in case 4 the work distribution between the mother vessel and work boats was made using the ratio of (90/80). • No crew optimization is performed. It is assumed that the scheduled maintenance of each turbine is done by a set of service personnels (defined as crew). The crew will not move to the next turbine until the maintenance on the previous turbine is completed. Moreover, for two work boats, two set of crews are required. Likewise in case 4, a set of three crews are necessary. • In case 2, 3 and 4 where the usage of mother vessel is employed; a check is necessary on the number of days a crew has been offshore. According to the UK offshore Health and Safety standards [49], a maximum of twenty-one days is allowed for a service personal to stay offshore. In order to comply with these safety standards, a check is made after every twenty days and on the twenty first day, a service boat is used to exchange the deployed crew with a new team of service personnels. The extra cost of access vessel transfer is added henceforth in the total O&M cost. • The mother vessel is ordered in advance and no lead time is associated with it. The maintenance is planned only after the mother vessel reaches the offshore wind farm. Hence, no travel or weather delays are computed when reaching the WF. Once the mother vessel reaches the WF, it is decommissioned only after the annual maintenance of the turbines is completed. • The weather time series is developed with the SCADA recordings of FINO 1 and FINO 2 MET mast.

7-2-4-2 Sensitivity and Veriﬁcation of the Simulation Results

To verify and evaluate the response of the model to diﬀerent input parameters and prove that the model behaves well to extreme scenarios, a sensitivity analysis is performed. The results are presented with respect to the complete WF for an annual scheduled maintenance. Following are some of the parameters for which the sensitivity analysis is performed:

1. Change in number of turbines (size of the wind farm)

Ashish Dewan Master of Science Thesis 7-2 Comparison Study of Logistic & Service Strategies 77

2. Change in the distance from shore to the farm 3. Change in the crew strength for schedule maintenance (crew eﬃciency) 4. Change in the month in which schedule maintenance was performed.

When doing the veriﬁcation, an arbitrary wind farm is considered, where the basic parameters necessary as simulation inputs are given in Table 7-7

Table 7-7: Simulation Inputs for the arbitrary wind farm (sensitivity analysis)

S No. Constant parameters for simulation Values (units) 1 Number of turbines 80 2 Type of turbine Siemens SWT-3.6 3 Hours of schedule maintenance 9 hours per turbine 4 Starting month of schedule maintenance June 5 Distance to the wind farm 30 km 6 Speed of the service work boat 18 knots 7 Service personnels per work boat or per turbine 6 W :12 m/s, W :1.5 m (boat) 8 Weather thresholds (Generated from FINO 1) ST HT WST :15 m/s, WHT :4.0 m (mother vessel) 9 Man hour cost 70 Euro per hour Service work boat renting cost (e.g. Katrine) 3000 Euro per day (approximately) 10 Mother vessel renting cost (e.g. Sea Energy) 36,360 Euro per day (approximately) 11 Wind farm capacity factor 0.3 12 Electricity tariﬀ (for supply to grid) 0.12 Euro per kWh

The performance parameters to compare the four cases are: Access Vessel costs (based on the number of days the vessel is rented), Man Hour costs (based on the number of hours the crew worked) and the loss of revenue due to the downtime of the turbines . The summation of access vessel costs and man hour cost is the total O&M cost. The overall cost incurred is the addition of total O&M costs and the lost revenue. Each of the veriﬁcation case is explained in the following sections.

7-2-4-3 Sensitivity Veriﬁcation Test: Distance from Shore

In Section 7-2-4, it is addressed that the WFs planned for future installations would be quite far off from the shore. Hence, the impact on the overall cost is important. Simulation with change in distance (from 30 km to 120 km) for each of the four cases is analysed to explore the best access strategy for performing the scheduled maintenance of the wind farms, the performance parameter being the overall cost. From Figure 7-11, one can observe that case 1 with two work boats is affected the most with distance to the farm. Maximum shift hours are consumed in travelling to and from the farm, which influences the actual working hours of the crew. In case 2, where the mother vessel alone was employed, it is noted that there is hardly any effect of distance. This is due to the high threshold limits of the mother vessel which made the working possible under extreme conditions. Also, the mother vessel is always situated offshore; therefore, no time is lost in travelling to shore. While comparing different cases of

Master of Science Thesis Ashish Dewan 78 Model Veriﬁcation and Analysis

Figure 7-11: Sensitivity with Distance

mother vessel deployment, case 4 is the most cost eﬀective. The reason is the maximum usage of the resources available; two daughter crafts and the mother vessel itself. Also, it is interesting to note that after a certain distance (with travel time more than 1 hour), the usage of mother vessel is justiﬁed, provided the other input parameters of the WF are kept constant.

7-2-4-4 Sensitivity Veriﬁcation Test: Selection of period for Schedule Maintenance

With the weather time series developed from FINO 1 MET mast, the month of June is selected for performing scheduled maintenance. Moreover, the necessity to choose the right month influences the overall downtime and number of days it takes for performing the maintenance operations. With this test, the starting time of performing scheduled maintenance is varied from January to December and the impact it creates in terms of overall cost for each of the cases is observed. As anticipated, case 2 is the least affected by the month in which the preventive maintenance is carried out. In the other cases (1, 3 and 4) where the work boat or daughter crafts is used; the influence is quite visible. Moreover, there is a common trend observed, where in, the months from August to November, the cost increases in all the three cases. It is certainly due to the extreme weather conditions prevalent during these months, where the work boat is not able to resume normal operations.

7-2-4-5 Two Parameter Sensitivity Veriﬁcation Test: Distance from Shore and Number of Turbines

As observed earlier in Section 7-2-4, the distance does influence the overall cost incurred because of O&M. Together with the distance, the size of the wind farm also influences the decision for choosing the right access strategy. A simulation is prepared to see the effect of both distance and number of turbines on the overall cost.

Ashish Dewan Master of Science Thesis 7-2 Comparison Study of Logistic & Service Strategies 79

Figure 7-12: Sensitivity Veriﬁcation with the month of Scheduled Maintenance

The distance is changed in steps of 30 km as is done in the above individual veriﬁcation test. The number of turbines is changed from 10 to 120 in steps of 10. The graphs and tables only for two cases (case 1 and case 4) are drawn, tabulated and compared. The rest of the cases could also be obtained similarly.

Figure 7-13: Sensitivity with Distance and Number of turbines (Case1)

As foreseen, in case 1, with increase in distance and number of turbines, the cost also increased linearly. While in case 4, the eﬀect is not entirely linear. Though, with increment in number of turbines, the daily cost of renting the mother vessel surged, the distance does not encourage any eﬀect on the overall costs.

Master of Science Thesis Ashish Dewan 80 Model Veriﬁcation and Analysis

Figure 7-14: Sensitivity with Distance and Number of turbines (Case4)

7-2-4-6 Parameter Sensitivity on Total Cost of Maintenance

To better understand the sensitivity of all the four parameters together on the overall cost (sum of O&M and lost revenue); a gradient analysis is performed for each of the parameters (for case 1 and case 4) and a pie chart (Figure 7-15) is drawn to show the impact of a unit increase in each parameter on the total cost of maintenance. Again, only two cases (case 1 and case 4) are compared. Similar analysis could also be done for rest of the cases.

Figure 7-15: Gradient Analysis: Case 1 [L] and Case 4 [R]

From the above two cases, it can be realized that the impact of per unit increment in crew strength is the maximum. Also, the month of performing scheduled maintenance is of some importance to the overall cost. Finally, as expected; the distance has the minimum impact in the case of mother vessel. Whereas for work boats, it has more impact than the number of turbines. This section provided eﬀective and innovative logistic, crew and access strategies and

Ashish Dewan Master of Science Thesis 7-3 Extreme Value Testing 81

the ways the O&M model handles each scenario. Also, the results are analysed with cost as the parameter of comparison. The next section brieﬂy explains further robustness of O&M model and its response in handling extreme user inputs.

7-3 Extreme Value Testing

It is necessary that the model designed should be able to counter extreme inputs that are passed by the user. Two diﬀerent circumstances have been discussed below to further validate the model robustness.

7-3-1 Service Model: Distance from shore for Access Vessel Operation

In this case, the WF is considered to be situated 400 km from shore. With the current work boat speciﬁcation, it is impossible for the crew to travel that far as the working period ends before they could even arrive. But in the case of mother vessel (discussed in 7-2-4-3), there is no impact of weather as the mother vessel is situated far oﬀshore itself. The model will give the following outputs for case 1 and case 2.

Table 7-8: Extreme value testing for distance from shore

Case 1 Work boats Warning Message 1 Case 2 Mother Vessel Total Cost (3.5 Million Euro)

7-3-2 Service Model: Maximum Crew allowed on a Vessel

Further, the model also asks the user to insert a maximum limit to the number of crew members allowed to travel on a work boat or a mother vessel. Again, for case 1 and case 2, a warning is conveyed to the user to correct the inputs based on the maximum permissible personnels allowed on either of the access vessels. Say for example, the maximum limit of a standard work boat is twelve personnels and that for a mother vessel is sixty personnels.

Table 7-9: Extreme value testing for Crew Strength

Case 1 Work boats Warning Message 2 Case 2 Mother Vessel Warning Message 3

1Twice travel time and planning time does not ﬁt in minimal weather window length! Please increase minimum weather window length and rerun. 2Maximum limit of the crew reached in the work boat. Please remove personnels for a smooth and safe travel to wind farm. 3Maximum limit of the crew reached on the Mother Vessel. Please remove personnels for a smooth operation.

Master of Science Thesis Ashish Dewan 82 Model Veriﬁcation and Analysis

7-4 Implementation of Model for Planned Wind Farm

In the above sections, the modelled working, its verification and sensitivity is discussed. In this section, the model is used to make a decision for choosing the right access vessel strategy or specifically to justify the usage of mother vessel. Two German and one UK offshore wind farm are selected for the analysis. For the farms of North Sea, data of FINO 1 met mast is used for weather predictions. For Baltic sea, weather conditions from FINO 2 met mast is adopted. The following table lists the wind farms chosen and their characteristics needed for model input.

Table 7-10: Input Parameters for planned WFs

S No. Constant parameters for simulation EnBW BALTIC 2 Borkum Riﬀgrund I Gwynt y Mör 1 Number of turbines 80 77 106 2 Type of Turbine Siemens SWT 3.6 Siemens SWT 3.6 Siemens SWT 3.6 3 Weather Information (MET mast) FINO2 (Baltic Sea) FINO1 (North Sea) FINO1 (North Sea) 3 Hours of Schedule Maintenance 9hours per turbine 9hours per turbine 9hours per turbine 4 Starting Month of Schedule Maint. June June June 5 Distance to the wind farm 32 km 55 km 18km 6 Speed of the Service Work boat 18knots 18knots 18knots 7 Service personnels per turbine 6 6 6 WST :12m/sec, WHT :1.5m (boat) WST :12m/sec, WHT :1.5m (boat) WST :12m/sec, WHT :1.5m (boat) 8 Weather Thresholds WST :15m/sec, WHT :4.0m (mother vessel) WST :15m/sec, WHT :4.0m (mother vessel) WST :15m/sec, WHT :4.0m (mother vessel) 9 Man Hour Cost 70 Euros per hour 70 Euros per hour 70 Euros per hour Service Work boat renting cost (e.g. Katrine) 3000 Euro per day 3000 Euros per day 3000 Euro per day 10 Mother Vessel renting cost (e.g. Sea Energy) 36,360 Euros per day 36,360 Euro per day 36,360 Euro per day 11 Wind Farm Capacity Factor 0.3 0.3 0.3 12 Electricity Tariﬀ (for supply to grid) 0.12 Euro per kWh 0.12 Euro per kWh 0.12 Euro per kWh

For the three wind farms, all four cases of access vessel strategy is simulated. The results after implementing the test case can be seen in Figure 7-16.

Figure 7-16: Comparison of Strategies for three planned wind farms

From the figure above, the future operator can make a wise decision for choosing the right strategy. For example, for EnBW Baltic2 WF, the usage of work boats (case 1) will still be the most cost effective strategy. For Borkum Riffgrund I , the use of mother vessel will be definitely be an advantageous proposition. For Borkum Riffgrund I , even the usage of mother vessel alone (case 2) will be still cheaper than the deployment of

Ashish Dewan Master of Science Thesis 7-4 Implementation of Model for Planned Wind Farm 83

work boats (case 1). However, for Gwynt y Mör, the results for case 1 and case 4 are almost comparable and the operator can choose the suitable strategy for his wind farm. Overall, one can clearly observe the impact of the distance from shore and number of turbines which lead to the better cost eﬀective results for case 4.

Master of Science Thesis Ashish Dewan 84 Model Veriﬁcation and Analysis

Ashish Dewan Master of Science Thesis Chapter 8

Conclusions

In this work, a MATLAB simulation model for the logistic and service aspects of offshore wind farm is developed. The tool performs Monte Carlo simulation for both the logistic and service model separately and suggests an optimized and cost-effective strategy for a given wind farm. It presents a sound integration of spare part inventory theory and time based stochastic modelling. A Graphical User Interface (GUI) is also implemented for the simplified usability. The logistic model initially evaluates the cost corresponding to a worst case scenario of storing and maintaining spare parts following a single-item approach. Later, the right inventory levels are obtained for a cost-effective strategy. It is observed that for the critical and expensive spare parts, the holding and backorder cost influence the ordering and storing policies and hence, a base stock policy or the (S − 1,S) policy is preferred both at the depot and central workshop. However, later in one of the comparison studies, the implementation of (R,Q) policy at the central workshop is explored and justified. With the increase in the number of wind farms by the same operator or the turbine manufacturer, the central workshop should cater to the needs of the multiple depot following a (R,Q) ordering policy. The optimized stock levels suggested by the logistic model are further used as an input to the service model. The service model primarily focusses on the replacement of the spare parts. It aims for improving the existing access and crew strategy followed by the maintenance team. Ordering of a contract vessel at the right time directly affects the support cost for a project. Optimization of the ordering time of the access vessel is done with respect to the spare part availability and subsequently the weather prediction. Even, an overall difference of five days in hiring a Jack-up barge can save approximately 0.1 million Euro. The Mean Time To Repair (MTTR) has been modelled according to Lognormal distribution in order to incorporate the random repair time which certainly depicts the reality. The Mean Waiting Time (MWT) in majority influences the availability of the system. Hence, innovative and appropriate crew strategies like full-shift and efficient- shift patterns are proposed. It is found that following an efficient-shift pattern can be highly profitable in terms of the O&M cost incurred. Also, a possibility of an offshore

Master of Science Thesis Ashish Dewan 86 Conclusions

accommodation is investigated and profits earned at various distances to the farm is estimated. Even at 30 km, a profit of 20 million Euro is possible. This calculation ignores the bank loans and other operating costs which will influence and decrease the profit margin to a more realistic but considerable value when taken into account. Further, night staying decision at the contract vessel is also looked at. Again, for a wind farm with a distance of 30 km from shore, the option or the decision to stay on-site at night looks reliable with an overall profit of 21 million Euro. Another promising vessel strategy using Mother vessel is compared. Different possible arrangements undertaking scheduled maintenance is checked. It is found that the Mother vessel is a better option than the conventional work boats for future wind farms with a distance from shore of 50-60 km considering its workability in the extreme weather conditions and facilities on-board. These innovative and proposed strategies are thereby useful for the planned wind farms which are farther and bigger from the ones referred in the project. Overall, the outputs produced by the Logistic and Service model are highly dependent on the precision of the reliability data, meteorological information and price slabs assumed in the inputs, indeed following the rule: “Model outputs are as good as the inputs”. With the developed O&M model, accurate inventory stock levels, downtime, availability, service and logistic parameters and hence the inventory and maintenance cost is obtained. The results from sensitivity analysis, comparison studies, extreme value testing and implementation with a planned wind farm delivers confidence in the modelling approach and the assumptions made in this work. However, appropriate inputs for a specific wind farm will help to improve the confidence in the model outputs.

Future Work The stochastic model developed is from the scratch and hence certain simpliﬁcations were made to implement the same. One of the necessary improvements to be incorpo- rated is the check of model behaviour against a more speciﬁc wind farm with reliability, weather and other associated support data. The logistic model assumed repairable and non-repairable spares both as consumables. A better understanding is still required of how the service team should handle the faulty spare part. Another possibility with the logistic model is the incorporation of multiple depots fro the wind farm which don’t have central workshop in the close proximity. Moreover, the modelling only considers the critical nacelle components. It will be worth- while to expand to the other balance of plant, foundation and cabling system, besides including less expensive and critical spare parts.

Ashish Dewan Master of Science Thesis Appendix A

Logistic & Access Vessel Delay Decision Flow

Master of Science Thesis Ashish Dewan 88 Logistic & Access Vessel Delay Decision Flow Flowchart for estimating the MLDT and Access Vessel Delay Figure A-1:

Ashish Dewan Master of Science Thesis Appendix B

GUI Implementation

Figure B-1: GUI for Logistic & Service Model

B-1 Features of GUI:

• In order to improve usability and comfort, a GUI is prepared using GUIDE (Graph- ical User Interface Development Environment) in MATLAB. • The inputs for both the logistic and service model are loaded (Load Inputs) by the GUI. Two individual buttons are provided for instructing the GUI to simulate

Master of Science Thesis Ashish Dewan 90 GUI Implementation

either the logistic (Logistic Optimization) or the service model (Service Optimiza- tion). • Besides the two simulation buttons, there are two other buttons, Default values and Reset values to give the user an idea of the values that are required to be entered. • The results after implementing any of the two simulation is saved in the workspace as a .mat ﬁle.

Ashish Dewan Master of Science Thesis Appendix C

Spare Part Data

C-1 Maintenance & Repair Report (WMEP)

Master of Science Thesis Ashish Dewan 92 Spare Part Data

C-2 Spare Part Data

Spare Code Spare Name Spare Price Lead time TAT Type (Number) (Description) (EUR(103)) (Weeks) (Weeks) (Repairable/Discardable) 1 Hub Body 71 10 DU 2 Rotor Blades 101.33 15 DU 3 Blade Bolts 2 2 DU 4 Aerodynamic Brake 10.5 2 5 LRU 5 Generator 100 10 10 LRU 6 Generator Windings 11.5 5 DU 7 Generator Brushes 1.21 1 5 LRU 8 Generator Bearings 1.21 1 DU 9 Transformer 48.06 10 10 LRU 10 Anemometer 0.71 1 5 LRU 11 Vibration Switch 0.71 1 DU 12 Temperature Sensor 0.71 1 DU 13 Power Sensor 6.96 1 5 LRU 14 Electric Control Unit 1.5 2 5 LRU 15 Relays 1 1 DU 16 Gearbox 191.5 10 10 LRU 17 Gearbox Bearings 1.21 1 DU 18 Gear Shaft 2.68 5 DU 19 Brake Disc 1.43 1 DU 20 Brake Pads 1.43 1 DU 21 Brake Shoe 1.43 1 DU 22 Main Bearings 2.6 5 DU 23 High Speed Shaft 2.68 2 DU 24 Couplings 1.43 2 DU 25 Hydraulic Pump 1.12 2 DU 26 Hydraulic Pipes 0.07 1 DU 27 Yaw Bearings 4.84 2 DU 28 Yaw Motor 0.97 1 DU 29 Hydraulic Cylinder 16.12 2 10 LRU

Ashish Dewan Master of Science Thesis C-3 Failure Rate/Demand Rate for Corrective Replacements 93

C-3 Failure Rate/Demand Rate for Corrective Replacements

Spare Code Spare Name Failure Rate/year/turbine Number Description Corrective Replacements 1 Hub Body 0.000123144 2 Rotor Blades 0.001786619 3 Blade Bolts 0.00001 4 Aerodynamic Brake 0.000800492 5 Generator 0.002341027 6 Generator Windings 0.00001 7 Generator Brushes 0.000431113 8 Generator Bearings 0.000800697 9 Transformer 0.003388375 10 Anemometer 0.003018165 11 Vibration Switch 0.00012314 12 Temperature Sensor 0.001786011 13 Power Sensor 0.00001 14 Electric Control Unit 0.008203073 15 Relays 0.000554166 16 Gearbox 0.00209395 17 Gearbox Bearings 0.000369468 18 Gear Shaft 0.00001 19 Brake Disc 0.00001 20 Brake Pads 0.000554142 21 Brake Shoe 0.000184719 22 Main Bearings 0.000430994 23 High Speed Shaft 6.16E-05 24 Couplings 0.000123177 25 Hydraulic Pump 0.002525134 26 Hydraulic Pipes 0.00147798 27 Yaw Bearings 0.00001 28 Yaw Motor 0.000677304 29 Hydraulic Cylinder 0.001170057

Master of Science Thesis Ashish Dewan 94 Spare Part Data

Ashish Dewan Master of Science Thesis Appendix D

MTTR Data Processing

D-1 Estimation of MTTR

Table below shows the raw MTTR values in minutes as obtained from the turbines, where complete replacement is required in the case of Generator (as a spare part). Again, only turbines with 1 1MW are chosen. The ﬁrst six numbers are the scheduled replacement times and the rest reﬂect the corrective replacements (in minutes).

Table D-1: Raw MTTR values (in minutes)

300 360 480 720 1440 9720 120 120 3360 326 120 937 3180 3845 518 493 240 120 360 7470 375 1237 231 1002 1014 381 2518 214 655 2040 180 1260 932 231 116 761 251

If we just take the average of the above dataset, the mean would be approximately 1287 minutes (21.5 hrs). This value if taken as a deterministic average would be inappropriate as we can see a lot of variance from the above raw data. Further, the procedure as described in [33] is followed to calculate MLE (Mean) and MLE (variance). This comes out to be 6.401171253 and 1.406809391. Finally, the Mean of the lognormal distribution (µ) and the variability of time to repair (σ) are computed. In this case, this is found to be 20.2 hours and 35.63 hours respectively. These values are used as inputs for the simulation model. Similar procedure is followed for each of the selected 29 spare parts. The input Mean and Standard Deviation for each of the spares is tabulated in Appendix D-2. It was mentioned in section 3-6 that lognormal distribution is commonly used to describe the MTTR behaviour. The justiﬁcation for the selection of lognormal distribution is made here using the above data set. The values are grouped to plot a graph between the frequency of occurrence and the Time to Repair.

Master of Science Thesis Ashish Dewan 96 MTTR Data Processing

Table D-2: Frequency of MTTR Values

MTTR (minutes) Frequency <200 3 200-400 9 400-800 6 801-1200 4 1201-1600 3 1601-2000 0 2001-2400 1 2401-2800 1 2801-3200 1 3201-3600 2 3601-4000 1

Figure D-1: Sample Lognormal distribution of MTTR

The corresponding graph with the above grouping is drawn as in ﬁgure below. The plot above is clearly ﬁtted to lognormal distribution. The low frequency initially represents the short duration repair time, a large number of observations closely grouped about the modal value and long repair time data points with the lowest frequency.

Ashish Dewan Master of Science Thesis D-2 MTTR Values as Inputs 97

D-2 MTTR Values as Inputs

Spare Name MTTR St.Dev. Hub Body 35.65735 88.03108 Rotor Blades 42.09059 78.85678 Blade Bolts 7.071451 1.744974 Aerodynamic Brake 28.45108 42.61847 Generator 20.29209 35.62929 Generator Windings 15.64578 9.615718 Generator Brushes 15.64578 9.615718 Generator Bearings 15.5931 2.505208 Transformer 17.52929 21.24886 Anemometer 9.048372 9.527484 Vibration Switch 3.894441 1.56305 Temperature Sensor 5.741223 7.610355 Power Sensor 3.894441 1.56305 Electric Control Unit 16.73822 32.34105 Relays 9.816413 7.707052 Gearbox 18.34502 24.26557 Gearbox Bearings 8.15527 5.552488 Gear Shaft 8.15527 5.552488 Brake Disc 9.6 0 Brake Pads 9.932749 10.49062 Brake Shoe 13.74674 20.47313 Main Bearings 25.94129 37.28562 High Speed Shaft 42.10978 73.14047 Couplings 13.42983 10.64419 Hydraulic Pump 8.569984 5.202622 Hydraulic Pipes 7.448466 5.144818 Yaw Bearings 5.97309 11.90605 Yaw Motor 15.7162 8.822295 Hydraulic Cylinder 11.30145 11.86526

Master of Science Thesis Ashish Dewan 98 MTTR Data Processing

Ashish Dewan Master of Science Thesis Appendix E

Support Organisation Information

E-1 Reference Wind Farms (Depot Locations)

Figure E-1: Egmond aan Zee (OWEZ) Wind Farm operated from the depot Ijmuiden (near Wijk aan Zee), Netherlands [7]

Master of Science Thesis Ashish Dewan 100 Support Organisation Information

Figure E-2: Nysted (Rødsand) Wind Farm operated from the depot Port of Rodby/ Gedser (small ferry harbor), Denmark [7]

E-2 Reference Wind Farms (Central Workshop to Depot Dis- tance Estimates)

Figure E-3: Estimated distance between the Main Central Workshop of Vestas (Randers), Den- mark and the local depot (IJmuiden), Netherlands- for OWEZ wind farm (Google Maps)

Figure E-4: Estimated distance between the Main Central Workshop of Siemens (Brande) and the local depot (Port of Gedser), Denmark - for Nysted Oﬀshore wind farm (Google Maps)

Ashish Dewan Master of Science Thesis E-3 Access Vessels employed for Maintenance 101

E-3 Access Vessels employed for Maintenance

Figure E-5: FOB Lady for Class B/Class E repair

Master of Science Thesis Ashish Dewan 102 Support Organisation Information

Figure E-6: Crane Vessel for Class C repair

Figure E-7: Jack-up barge for Class D repair

Ashish Dewan Master of Science Thesis Appendix F

Weather Time Series Preparation

F-1 Met Mast Data Sheet

Table F-1: Characteristics of MET masts

FINO 1 FINO 2

Operator FuE-Zentrum Fachhochschule Kiel GmbH Schiﬀahrtsinstitut Warnemünde e.V. Location North Sea, 45 km north of Borkum island Baltic Sea, 39 km north of Rügen island Coordinates N 54◦ 0,86’ E 6◦ 35,26’ N 55◦ 0,42’E 13◦ 9,25’ Water depth 30 m 20 m Comissioning 2003 2007 Research focus Meteorology, hydrology, ecology Meteorology, ecology (noise protection), sea traﬃc studies

F-2 Weather Time Series Processing

Before Data Processing, the wind time series looked like as shown in Figure below: Clearly, there are a lot of irrelevant values (-999) and missing values observed with the available time data obtained from SCADA. Following are the necessary steps followed for processing the above raw time series.

1. The raw wind speed data for 5 years is imported in MATLAB. 2. The invalid (-999) and the missing data recorded by SCADA is removed and the remaining values (valid data) is used to generate the Weibull parameters (scale and shape factor). 3. With the obtained Weibull parameters, a random time series equal to the length of initially imported vector is generated.

1Source: http://www.oﬀshore-windenergie.net/en/research/federal-research-programme/ﬁno-1-3

Master of Science Thesis Ashish Dewan 104 Weather Time Series Preparation

Figure F-1: Wind Time Series before processing

4. The missing and the irregular values are plugged in with the random time series obtained from the Weibull parameters. 5. Since the time series required to be passed as model input is hourly based, the average of six consecutive 10 minute readings is taken. The same is done for the entire 5 year data of FINO2. 6. This way a 5 year wind speed time series for FINO 2 is created.

After correction, the treated time series is seen as in ﬁgure.

Figure F-2: Wind Time Series after processing

Ashish Dewan Master of Science Thesis Appendix G

Class of spares-Type of Maintenance

Master of Science Thesis Ashish Dewan 106 Class of spares-Type of Maintenance

Spare Code Spare Name Classes for type of maintenance 1 Hub Body Class D 2 Rotor Blades Class D 3 Blade Bolts Class C 4 Aerodynamic Brake Class C 5 Generator Class D 6 Generator Windings Class C 7 Generator Brushes Class C 8 Generator Bearings Class C 9 Transformer Class D 10 Anemometer Class C 11 Vibration Switch Class B 12 Temperature Sensor Class B 13 Power Sensor Class B 14 Electric Control Unit Class B 15 Relays Class B 16 Gearbox Class D 17 Gearbox Bearings Class C 18 Gear Shaft Class C 19 Brake Disc Class B 20 Brake Pads Class B 21 Brake Shoe Class B 22 Main Bearings Class D 23 High Speed Shaft Class C 24 Couplings Class C 25 Hydraulic Pump Class B 26 Hydraulic Pipes Class B 27 Yaw Bearings Class C 28 Yaw Motor Class B 29 Hydraulic Cylinder Class C

Ashish Dewan Master of Science Thesis 107

The explanation of each Class Types is as follows:

• Class A Requirements: No Spare part + Access Vessel + 2 Crew members (This is not included in our study) • Class B Requirements: Spare part + Access Vessel (boat) + 3 Crew members. (Use of permanent internal crane for replacement) • Class C Requirements: Spare part + build-up crane with a vessel + Access Vessel (boat) + 6 Crew members. • Class D Requirements: Spare part + Access Vessel (boat) Access Vessel (Jack- Up barge) + 6 Crew members.

Master of Science Thesis Ashish Dewan 108 Class of spares-Type of Maintenance

Ashish Dewan Master of Science Thesis Appendix H

Event List

Master of Science Thesis Ashish Dewan 110 Event List Event List generated as an output from Service Model Table H-1: 123456 157 268 149 25 10 14 10 2 8 2 15 2 2 3 2 3 3 0 2 0 1 0 1 1 2 1 4 6 0 0 7 10 12 4446 121966 15 14572 18 18 66346 88799 32098 135128 154427 0 47471 0 0 0 0 0 0 0 0 20 5 50 8 25 37 4.499640029 5 0.899928006 11.69906407 17 108 12 272 2.699784017 0.223542117 122 8.099352052 0.074514039 115 12 0.173866091 120 84 2.699784017 264 0 52 55 79 79 0 68 6 2 0 13 14 60 4 2 10 1 6 0 0 2 0 24 0 0 24 24 122510 4669 0 14822 89047 32342 66371 135159 154543 47627 1011121314 2915 251617 218 519 4 1420 14 3 10 2 14 29 4 10 4 3 2 2 3 1 2 0 3 3 0 1 1 1 18 1 20 1 23 1 23 1 25 138562 1 25 10910 26 27 74928 135240 28 162167 28 71569 33 3016 0 146860 82929 0 115975 0 67538 0 0 0 0 0 0 4 0 9 0 169 16 32 0.099352052 14 2.699784017 30 14.69546436 19 228 2.099352052 75 40 0.223542117 19 306 2.699784017 30 9 139 8.099352052 0.198704104 225 17 15.2987761 0.173866091 16 72 158 86 0.124190065 3 106 312 24 2 0 28 136 28 21 2 216 4 1 6 16 4 24 6 10 168 9 0 6 24 168 2 0 24 139046 0 75679 0 10980 24 135466 162473 24 147204 71607 3179 83557 116050 67618 Replacement No Spare code Spare Maintenance Class Spare Part Type Turbine ID Service Start Time Logistic Delay(MLDT) Repair Time (MTTR) Travel Delay Shift Delay Weather Delay Planning Delay Access Vessel Delay Turbine Restart Time

Ashish Dewan Master of Science Thesis Bibliography

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Master of Science Thesis Ashish Dewan 112 Bibliography

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Ashish Dewan Master of Science Thesis 113

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Master of Science Thesis Ashish Dewan 114 Bibliography

Ashish Dewan Master of Science Thesis