Renewable and Sustainable Energy Reviews 136 (2021) 110414
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Renewable and Sustainable Energy Reviews
journal homepage: http://www.elsevier.com/locate/rser
Reliability, availability, maintainability data review for the identificationof trends in offshore wind energy applications
D. Cevasco *, S. Koukoura, A.J. Kolios
Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow, G4 0LZ, United Kingdom
ARTICLE INFO ABSTRACT
Keywords: This work presents a comprehensive review and discussion of the identification of critical components of the Onshore and offshore wind turbines currently installed and next generation of offshore wind turbines. A systematic review on the reliability, avail Reliability ability, and maintainability data of both offshore and onshore wind turbines is initially performed, collecting the Maintainability results from 24 initiatives, at system and subsystem level. Due to the scarcity of data from the offshore wind Availability industry, the analysis is complemented with the extensive experience from onshore structures. Trends based on Critical components the deployment parameters for the influence of design characteristics and environmental conditions on the onshore wind turbines’ reliability and availability are first investigated. The estimation of the operational availability for a set of offshore wind farm scenarios allowed a comparison with the recently published perfor mance statistics and the discussion of the integrity of the data available to date. The failure statistics of the systems deployed offshore are then discussed and compared to the onshore ones, with regard to their normalised results. The availability calculations supported the hypothesis of the negative impact of the offshore environ mental conditions on the reliability figures. Nonetheless, similarities in the reliability figures of the blade adjustment system and the maintainability of the power generation and the control systems are outlined. Finally, to improve the performance prediction of future offshore projects, recommendations on the effort worth putting into research and data collection are provided.
1. Introduction wind turbines, compared to modern installations and offshore trends. Nonetheless, the collection of historical data has shown to be useful for Despite the efforts to achieve a through-life reliable design and at benchmarking critical components to support monitoring concept tempts to control the failures of wind turbines, some system failures are development and a systematic service life performance analysis. inevitable. The inherent requirement for either cost, or material and weight optimisation, together with the extreme operating conditions, 1.1. Previous review works can lead to unexpected failures. This is true for land-based turbines and has an even greater impact on offshore wind systems, where the harsh The onshore and the few offshore available data have been already environment and the high cost of the assets and logistics increase the analysed, and cross compared by several authors. In 2011, Sheng and importance of a proactive approach to the system’s maintenance. Since Wang [9] compiled the first extensive survey of the various databases the early-stage wind farms, a considerable effort has been made in col available until that year. Three more recent studies have significantly lecting indicators for their reliability, maintainability and availability contributed to gathering and comparing the data available until the year (RAM) statistics and putting them into databases [1]. National and in 2018. Pfaffel et al. [2] presented a comprehensive collection of the ternational initiatives have been mainly directed at creating repositories to-date available RAM statistics (a total of 23, of which 20 are onshore), for onshore wind turbines [2]. Only recently have some initiatives updating the historical data comparison initiatives with the results from focused on the collection of RAM data from modern [3,4] and/or offshore wind farms, and including datasets from outside the European offshore systems [5,6]. Although large and heterogeneous, the pop continent. The failure frequencies and downtime are presented in an ulations of some of the most well-known campaigns (e.g. Ref. [7,8]) all-in-one comparison according to standardised key performance in generally include statistics of outdated configurations and small rated dicators (KPIs), in their normalised and non-normalised form. Artigao
* Corresponding author. E-mail address: [email protected] (D. Cevasco). https://doi.org/10.1016/j.rser.2020.110414 Received 30 January 2020; Received in revised form 21 September 2020; Accepted 21 September 2020 Available online 9 October 2020 1364-0321/© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). D. Cevasco et al. Renewable and Sustainable Energy Reviews 136 (2021) 110414 et al. [10] cross-compared some of these reliability statistics (13 initia commonalities and distinguishing correlation aspects between modern tives, of which two are offshore) with the purpose of identifying the and more early-stage assets. This paper adds to the existing body of critical components of wind energy converters across all technologies, knowledge on the identification of the trends at the turbine, its system sizes and locations, in order to suggest condition monitoring strategies, and subsystem level, based on the specific design parameters and the techniques and technologies. Similarly, Dao et al. [11] used the aver deployment conditions – either onshore or offshore, as well as site- aged statistics of [2] to visualise the trends in reliability and maintain specific parameters. This can subsequently allow the development of a ability figuresof offshore wind turbines, as opposed to the onshore ones, better understanding of the sensitivity of certain components and and assess their impact on operational cost, to assist operators to identify technologies to key design and environmental parameters, facilitating the optimal degree of reliability improvement to minimise the levelised technology qualificationof new alternatives and the reliability analysis cost of energy. of the units in a farm. The structure of the paper is organised as shown in Fig. 1. An over 1.2. Issues with data collection and comparison view of the RAM statistics from historical repositories for onshore and offshore systems, as well as from the more structured industry-led ini Previous studies identified some trends in the averaged data, tiatives, is given in Section 2. In Section 3, the methods for the uniform depending on the survey location (on- and offshore, and governing and consistent comparison of the statistics are presented. The RDS-PP® country), population size and mean power rating (e.g. Ref. [11]). designation is adopted to establish a uniform and easy comparison of the However, two main issues related to the use of cumulative statistics still results among the several datasets. The quality of the collected statistics exist: is discussed in Section 4.1 based on the type of records used for assessing failure of the turbine. In Sections 4.2 and 4.3, the trends from the 1) As highlighted by Leahy et al. [12], and previously by Sheng and comparison of the RAM statistics are identifiedbased on the design and O’Connor [13], the currently accessible RAM data lack a harmonised deployment parameters of the wind turbines. Finally, in Section 5, RAM practice for their collection, processing and publication. The absence figures of offshore wind assets are discussed. The operational avail of standardisation in the type of data, and methods for their collec ability is evaluated for a set of reference offshore wind farms, to quantify tion, leads to different levels of data quality. Furthermore, it is the impact of statistical uncertainties and unveil the challenges for the challenging to compare data among studies if project-specificand/or offshore wind industry in the collection of representative data. undocumented taxonomies are employed for the systems and sub systems characterisation. 2. Review of onshore and offshore statistics 2) As noticed in Ref. [1,14], technologies of different maturity, in different operating years, are expected to fail differently. The For the purpose of this paper, only fully operational data have been deployment location and the varying environmental conditions can collected, rather than test data of single components (e.g. gearbox reli play an important role in the lifespan reliability of the wind turbine ability collaboration [22] and blade reliability studies [23,24]). The systems and subsystems [15–17]. When comparing the statistics in RAM statistics found in the literature from single initiatives to summary terms of averages among the heterogeneous population [2,10,11], reports are analysed in more detail. As the type of data collected is this level of detail is not considered. heterogeneous, a terminology is introduced for describing and classi fying the findingsfrom the several databases. Standardised definitionsof To tackle the first issue, Leimeister at al. [14] recognised that fuzzy reliability, maintainability, availability and performance indicators are set and/or evidence theories can help deal with the uncertainties of summarised in the Appendix and are used to describe the data re vague data. Nonetheless, these methods cannot cope with the same level positories throughout the paper. of detail and information as for a RAM database. Starting from a chronological overview of the main initiatives for In 2011, the Continuous Reliability Enhancements for Wind (CREW) onshore systems, the main findingsare then outlined for the more recent Database and Analysis Program, supported by the US government, offshore data collections. Despite including insights on offshore risks, introduced a consistent approach for the collection of high-resolution the industry-driven databases (see Section 2.2) are generally not avail supervisory control and data acquisition (SCADA) data with the aim of able to the public for confidentialityreasons. Consequently, publications characterising the reliability and performance of the country’s fleet. from other independent authors are reviewed, to obtain an appreciation Motivated by the standardisation intent, the members of IEA Wind Task of the RAM experience of various offshore wind farms installed in Eu 33 created, in 2013, the “Reliability Data Standardization of Data ropean waters. Collection for Wind Turbine Reliability and Operation & Maintenance An alyses: Initiatives Concerning Reliability Data”. Similarly, industrially-led 2.1. Wind turbine reliability and performance statistics repositories are currently collecting data with a higher level of detail, adopting structured and harmonised procedures to accommodate the 2.1.1. Overview of onshore data different types of data from wind farms [18,19], while providing suffi One of the first reliability databases for onshore turbines was cient information for a consistent comparison of the structure per ty compiled by Lynette [25], who analysed the trend in availability and pology, age and location. costs for the maintenance of various types of small-scale units installed With regard to the second point, some research effort has been put in California until the end of the 1980s. Similarly, the Electric Power into finding a correlation between the turbines’ failure rates and the Research Institute (EPRI) collected, during 1986 and 1987, failure data associated environmental conditions [15,20]. In this regard, Barabadi for a portion of its Californian population [1]. These statistics were re et al. [21] suggested a methodology for RAM data collection of engi ported by the DOWEC (Dutch Offshore Wind Energy Converter) research neering structures in Arctic conditions, showing its applicability to the program in Refs. [26], which has been one of the pioneers in the offshore and marine industries. documentation of wind turbine reliability figures. Due to the outdated technologies in the population, the Californian data were not integrated 1.3. Scope of the analysis and methodology in their comparative study. Only the yearly statistics, recorded around the end of the 1990s and firstyears of the 2000s, by the largest European Having established the need for more representative RAM databases, data collection campaigns – the German and Danish Windstats news this work aims to perform a comprehensive review of existing published letter, and the German LWK (Land Wirtschafts-Kammer) and WMEP data related to reliability, availability and maintenance of onshore and (Wissenschaftliches Mess-und Evaluierungsprogramm) databases – were offshore wind turbines, with a view to critically discussing cross-analysed. By plotting the results per size classes (within the single
2 D. Cevasco et al. Renewable and Sustainable Energy Reviews 136 (2021) 110414
Fig. 1. Methodological framework of this paper.
initiatives) and across the databases, the DOWEC team simply observed Sandia Laboratories, has become more extensive and structured. a significant scatter in the trend, justifying through the unknown age and the outdated type of some installations the causes of their statistics’ 2.1.2. Overview of offshore data discrepancy. In contrast, some years later, Ribrant et al. [27,28] outlined Little failure data exist in the public domain for offshore wind sys some similarities in the share of the components to the turbines’ failure tems. Performance from UK’s offshore round 1 wind farms, with evi rate and downtime, when comparing the Swedish and Finnish databases dence of wind farms’ availability indicators (see Appendix A) and – Vindstat and Felanalys, and VTT, respectively – with the current capacity factors (CF), were first reported by Feng et al. [43]. In this WMEP results. work, as in Refs. [5], maintenance records and operational issues of four In the same period, a substantial contribution to the collection and selected wind farms were analysed. Similarly, the reliability figures for understanding of the turbine reliability statistics was made by Durham the Egmond aan Zee wind farm were derived by Crabtree et al. [44] by University (UK) and Fraunhofer IWES (Germany). For the first time, accessing the operational report from Noordzee Wind [45], for the first Tavner et al. [29,30] analysed and compared the time-trend of the three years from installation. In Ref. [44], they additionally updated the reliability results from EPRI, Windstats, LWK and WMEP, plotting them results from the early experience of round 1 wind farms, which were against the historical data from other industrial turbines. Tavner et al. affected by technological-immaturity failure events. Besides, they compared the components’ failures of the LWK population, grouping collected the performance indicators of round 2 wind farms, showing a them by layout and size [7,29,31]. Furthermore, Tavner et al. investi growth in the average CF for the more modern offshore wind turbines, in gated the effect of weather and location on the turbines’ failures, line with results presented by the SPARTA [46] and Offshore-WMEP drawing some preliminary conclusions on their correlation. In Ref. [15, [47] projects. 32] they observed a periodicity in the failure frequency for some of the With regard to reliability and maintainability data, one of the most Danish Windstat components, while in Ref. [20] they investigated the complete contributions is the dataset published by Carroll et al. [6], for a possible dependency of failures on the wind farm location for a popu population of 350 offshore wind turbines. Despite the results presented lation of Enercon E30-33 turbines. In parallel, and consistently with being from a single manufacturer, the detailed definitionof the failure, what is shown by Ref. [31–34], Faulstich et al. analysed the effect of the and the further results on the repair time, material costs, and required turbines’ configurationand location (onshore, coastal, and offshore) on technicians per subassembly, are provided. the reliability figuresof the WMEP database [33,34]. In Ref. [35], they In terms of availability, onshore wind turbines have been shown to additionally explored the possible link between WMEP failures and wind reach values in a range of 95–97% for modern systems [2]. For offshore speed, developing further the first observations of Hahn [36]. projects, however, the location and associated challenges (i.e. accessi From the experience of the above-mentioned databases, and because bility and exposure to extreme weather conditions) can considerably of the increasing number of wind farm installations in Europe, more lower availability. As observed by Ref. [46,47], older farms – comprising structured RAM data collections have been launched. Aimed at moving turbines with relatively low nominal capacity, and relatively close to the towards a design-for-reliability approach, and targeting improved con coast – exhibit an availability in the range of the onshore average one. dition monitoring techniques [8], the European ReliaWind project ran Newer farms – bigger and generally located further from the coast – are for three years from 2008. Starting from reviewing previous European characterised by an increase in maintenance efforts [48]. Despite the projects (EUROWIN and EUSEFIA [2]) and national-level initiatives, this higher CFs [44], the technical availability (AE, in Table A.2) of offshore project collected and analysed heterogeneous data from the turbines wind farms can fluctuate across the years, depending on the distance operation and maintenance (O&M) activities, based on the joint effort of from shore and hence the ease to perform the required maintenance industry (e.g. Siemens Gamesa), technical experts (Garrad Hassan, now operations [49]. Additionally, the fluctuation of AE among several sur DNV GL) and academia (Durham University, among others). Contem veys can be related to the varying maintainability of components for the porarily, in Germany, the Fraunhofer Institute continued the WMEP different wind turbines’ concepts and designs [50,51]. database activity in the “Increasing the availability of wind turbines” (EVW) project [37,38], which ended in 2015. Despite these projects 2.2. Industry-led RAM databases representing two of the most recent and complete databases from the European experience, due to confidentiality issues, only their final re Following the compilation of the first databases (e.g. LWK, WMEP) ports and relative values of the total statistics are accessible. and recognising the limits of these earlier data collection initiatives, From the first decade of the 21st century, other data collection ini several authors have suggested possible improvements to the techniques tiatives from the rest of the world have contributed to documenting and and processes used for gathering and analysing data (e.g. Ref. [42,52, tackling the reliability of onshore installed systems. Academic and in 53]). Among these, Hameed et al. [54] proposed an optimal RAM dustrial researchers in India, China and Japan published the first reli database to be applied to offshore wind turbines, identifying the short ability and availability statistics reports for specificwind farms [39–41] comings of the historical databases from both the onshore wind and the and turbine manufacturers [3]. The CREW Database and Analysis Pro offshore oil and gas (Offshore Reliability Data, OREDA) industries. gram [42], in the USA, which is an ongoing activity coordinated by Observing how the lack of standards associated with reliability data
3 D. Cevasco et al. Renewable and Sustainable Energy Reviews 136 (2021) 110414 adversely impacts industry progress in addressing the reliability issues, turbines by: the IEA Wind Task 33 started compiling recommended practices [55]. In line with these conceptual examples are two recently launched • Capacity (power rating). As several studies have already shown (e.g. RAM databases for onshore and offshore systems: Ref. [26,59,60]), the number of failures (per turbine and/or per component) can be dependent on the dimension of the turbines, • The SPARTA (System Performance, Availability and Reliability making it necessary to present the results by power class grouping. Trend Analysis) initiative [19], started in 2013 by The Crown Estate • Age of the installation. It is common knowledge that the failure rate is (UK) under the supervision of the Offshore Renewable Energy (ORE) a time-dependent variable [32]. Therefore, the age of the system, Catapult research centre. SPARTA is gathering KPIs (at wind farm whenever known, is reported to account for the possible influenceon level) and reliability figures (at subsystem level) from the partici the population statistics (e.g. infant mortality and end-of-life wear pating operators, outputting a monthly benchmark. out failure events). • The German equivalent, WInD-Pool (Wind-Energy-Information- • Technical concepts and drivetrain configuration.A deep understanding Data-Pool), with Fraunhofer IEE as trustee [18]. It can be seen as the of the results also comes from the knowledge of the type of structure successor to WMEP [33], where additional (but never published) and configuration analysed. It is proposed here to identify the information on the cost of the maintenance services was collected. It drivetrain layouts according to the concept classes, as presented in continues and merges the EVW (Erhohung¨ der Verfügbarkeit von Table 1. These configurationsare similar to those of [49]; however, Windenergieanlagen) [37,38] and Offshore-WMEP [56–58] research the sub-types acronyms are arranged to meet the concepts used by projects, gathering historic and recently collected data for both the WMEP [61] and LWK projects [31]. The generators’ description onshore and offshore wind turbines. and acronyms are retrievable from Refs. [62,63], respectively. Schematics of the configurations, can be found in Ref. [64]. 3. Methods The terminology introduced in Appendix A is used to state which To consistently compare the RAM statistics and unveil potential RAM indicators are provided in each reference. The level of detail re trends, three methods are employed in this study. First, a cataloguing ported is specified according to the conventions introduced in Table 2. activity is used to gather the information available from the literature using a standardised terminology for the statistics, the type of data and Table 2 the wind turbine typology. This process facilitates access to the statistics Legend for symbols adopted in the synoptic tables. and allows the evaluation of the completeness and quality of the data collected by each initiative. The adaptation of RAM repositories to a Symbol/ Description Acronym unique taxonomy based on the most widely adopted reference desig nation permits comparing data fairly across different initiatives. Finally, %* Available only as a percentage/share of the *, per component calculations are carried out to uniformly compare the onshore and (without total/absolute values) offshore reliability and maintainability data in terms of operational N Number of stops *(t) Information on the time distribution of the RAM variable (*) is (time-based) availability for a hypothetical offshore wind farm scenario. given
*i(t) Information on the time distribution of the RAM variable (*) is 3.1. Statistics cataloguing given, per component ✓/X Information given but not possible and/or easy to access ● 3.1.1. Cataloguing approach Findings for failure frequency (top three) ○ Findings for failure downtime (top three) To compile a comprehensive catalogue of the most important (and Ave Averaged information and/or results accessible) information for onshore and offshore RAM statistics, it is first Det Detailed results necessary to identify what characteristics are worth being collected. The Info Additional/other information database and the population size are usually reported to provide an WT/WF Wind turbines/farms in the population On Onshore installations indication of the statistical significanceof the data collected. However, Off Offshore installation consistency issues when discussing and comparing the results can arise from the lack of sufficient details on the population of wind turbines [26]. For this reason, the classification applied here differentiates the
Table 1 Wind turbine configuration types. Adapted from Refs. [60] and integrated with [61,64].
Concept Types Sub-Types Speed Control Gearbox Generator Grid connection
Danish A A0 Fixed (dual) Stall a Multi-stage c SCIG Capacitor Advanced Danish A1 Fixed Stall b A2 Pitch Variable-speed B Limited Variable d Pitch Multi-stage WRIG Capacitor C Variable Pitch Multi-stage DFIG Partial-scale power converter e D DI DImW Variable Pitch Multi-stage WRSG Full-scale power converter DImS SCIG DImP PMSG DI1P Single stage PMSG DD DDP Variable Pitch None PMSG DDE EESG Full-scale power converter f
a Passive stall regulation. b Active stall regulation. c Generally four-stages and up to two-stages gearbox. d With a variable resistance in the rotor windings. e Converter feed back to the generator. f Double feed back to the generator.
4 D. Cevasco et al. Renewable and Sustainable Energy Reviews 136 (2021) 110414
Even though a constant (averaged) value of the failure rate over time is have been developed in the past. Among these are the VTT components’ generally reported – based on the generally assumed homogeneous breakdown presented by Stenberg in Ref. [69], the SANDIA Laboratories Poisson process for the components’ useful life, information on the time taxonomy in Refs. [86], and the GADS (Generating Availability Data variance of the failure rate and the other RAM indicators is reported System) used for North America wind plants. Another two, more recent, where possible. It must be noted that, although the best match between sophisticated and comprehensive classifications are the ReliaWind the provided definitions and single initiatives terminology is pursued, project taxonomy (see e.g. Ref. [4,8]) and the RDS-PP® (Reference the classification is completed based on the engineering judgement of Designation System for Power Plants) taxonomy, published by the VGB the authors. PowerTech e.V. in Ref. [87]. The first was created for an extensive To implicitly suggest the definitionof failure used, the classification failure data analysis and is internationally recognised, while the second of the typology of the O&M data sources is specified if possible. As re one, also widely accepted, is currently employed for the offshore data ported by Kaidis et al. [65] the use of different methods for the data collection schemes of SPARTA and WinD-Pool (see Section 2.1.2). collection is associated with multiple RAM information and data quality. However, the ReliaWind complete taxonomy is not publicly available, While in the IEA Wind Task 33 [54], the four main groups of equipment, and there is no ongoing development to maintain it. In contrast, the operating, failure and maintenance/inspection data are distinguished, a RDS-PP® offers open access to the draft document [87], and a high level higher level of detail is here given by following the grouping of [65]. As of detail for both system and subsystem identificationand components’ for [12], the data sources are classified in Table 3. Furthermore, the technical information. For these reasons, the RDS-PP® was adopted to difference between the collection scheme typologies – either raw or re unify the statistical reliability and maintainability data in this analysis. sults data approach, as proposed by Ref. [2] – is additionally made, The information necessary for the adaptation of the taxonomies is whenever possible. accessed from the VGB PowerTech e.V. draft document [87], and sum To assist the understanding of incomplete and/or public-restricted marised in Table 8. analysis, an additional column is added for summarising the main find The authors of this paper mapped, to the best of their knowledge, the ings. The list of references is then included by specifying the type of initiative-specifictaxonomies to RDS-PP®. When a proper mapping was information given in the case of multiple citations. Other basic infor not possible, a higher share was given to the introduced “Other” cate mation (such as country, study period and database size) is also reported gory. On the other hand, the generic “electrical systems” category, for completeness. usually adopted in the earlier data collections, is integrated here into the transmission group. 3.1.2. Synoptic tables The characteristics of the 24 databases found in the literature were accessed either through the initiatives’ original publications (if 3.3. Availability calculation possible), or the review works mentioned in Section 3. Due to the low level of detail in some of these works, their cataloguing remained 3.3.1. Availability assessment tool partially completed, not providing a sufficient description for the clas The variance in the failure rate of the wind turbines results in a sification of some quantities. It should be noted that further analysis is variance in their estimated availability. This can affect decision making required to integrate the results from the European EUROWIN and for the accurate planning of future offshore wind projects. The open EUSEFIA projects [2], the Japanese NEDO initiative [2], and the O&M assessment tool, developed by the authors in Ref. [88], is Fraunhofer’s EVW and recently launched WInD-Pool database (see employed to estimate and investigate the impact of the different failure Section 2.2). Therefore, only the initiatives that could be fully accessed rates provided in the literature on the potential availability of an are reported in the synoptic from Tables 4–7. offshore wind farm. The tool was built with the aim of supporting the development of wind farms’ maintenance strategies. As for other O&M management tools [89,90], they have a modular structure consisting of 3.2. Taxonomy adaption the following core modules: (1) reliability, (2) power, (3) weather forecasting, (4) maintenance and (5) cost. The flowchartof the processes As for [2,10], it was necessary to select a uniform and convenient and steps of the tool are shown in Fig. 2. The inputs are weather data, language to identify the equipment in a wind turbine to coherently cost data, reliability data, turbine specificdata (power curve), wind farm compare the several statistics. As outlined by Ref. [54], different lists of layout data (distances from shore), and repair information, such as terms can be used for categorising aspects of components, failures, number and type of vessels, and the crew required for the restoring of maintenance tasks etc. as “taxonomies”. Several equipment taxonomies the system. The wind farm lifecycle and maintenance activities are
Table 3 Reliability and maintenance data sources and their characteristics. Adapted and extended from [54,[65]].
Type of data Information derived Notes (disadvantages)
(i) Maintenance logs (incident reports) • Accurate information on failures • Can be in hard copy (difficult to read and/or incomplete) • Information for downtimes • Cost of repair/replacement (ii) Operation and alarm logs • Number of stops • Unknown alarm code • Stops duration • Numerous stops for same failure • (No information on environment. conditions) • (No description of maintenance activity) (iii) a. 10-minutes • Failure data (frequency and downtime) • Large amount of data (consuming processing) SCADA b. SCADA • Information for further analysis (e.g. root cause analysis) • Not all alarms are associated with failures alarms • Environmental conditions • (No description of maintenance activity) (iv) Service provider bills • Maintenance cost • (No detailed information) • Indications of failures (v) Component purchase bills (work orders) • Cost information for components ’repair/replacement • (No information on failure data)
5 D. Cevasco et al. Renewable and Sustainable Energy Reviews 136 (2021) 110414 ] ], 69 67 ], [ from [ ] 27 [ 30 [ from (accessing stats ]) ] ] ] ] ] 66 71 71 (2009) 32 [ [ [ 68 28 [ , ] ] ] ] ] , ] ] 67 28 68 67 26 26 27 30 68 [ (2000/4) [ [ [ [ [ [ 2000/4 Ref. [ Det Info Ave/Det Info [ References ) at t ( sys. i Bill have * speed 2007 sys., control yaw sensors, other, Editor sys, – study from blades/ wind sys., statistics statistics sys., sys., elect. 2012. rotor 2003 of data sys. – sys. initiatives language) reported control blades/pitch, control, electric, yaw λ (2010), original on 2004 2004 gears overlap) Findings/ (in – – two sys. 2008 also ] WT the gears, gears, gearbox, electrical other, other, hydraulics, other, electrical 66 collected are 2000 (the some • blades/pitch o electrical Based Canter WindStats. • • In [ 2000 • pitch, o hydraulics • hydraulic Influence on • yaw Averaged and o generator, Main Observations data (t) O O X A X X X A CF X X X KPI DT (t) i DT X MDT (t) DT (%) X X X % MDT X X DT down time λ λ λ N λ (t) (t) (t) i i i (t) (t) (t) (t) (%) λ λ λ λ N X % (%) (%) λ (%) λ λ X Reliability/Performance λ (all (all ]) /X /X 70 X X X X X ✓ concepts) ✓ concepts) X Type [ Ref. in (early (info [year] 19 20 ]) ÷ ÷ 66 /X [ X ✓ 1 X X 1 fail. excluded) X X X Age Ref. in in ]) rating 1 70 (info (info [ characteristics ÷ 0.15 3.0 0.5 1.0 0.1 2.5 0.1 2.5 /X /X ≥ ≤ X ✓ Ref. < 0.5 > X X ✓ X ≥ ≤ ≥ ≤ X WT Power [MW] : : 71.2 ◦ ◦ (until (in n n 2012) 2006): ~5000 35 37 ~1200 published (in (in WTs WTs WTs WTs WTs WTs WTs 723 786 2004): 2001): 2001): 2008) 2004): 2004): 4285 – – – – – – s s ◦ ◦ ◦ ◦ ◦ ’ – ’ (for n n n n n MW: MW: 2009) WFs WTs WTs WTs WTs 1 1 ◦ ◦ ◦ ◦ ◦ (in Ave N Ave N N ~7000 2008 ~20,000 (2000 N 2005): 2005): (1999 ~2000 (1999 ~2750 ~92 N (1996 < ≥ (1994 2345-851 Ave (1994 1291 1999 Denmark: Germany: ~24,000 Sweden: Size results) Ave Ave from at type reports. results) and and and ] 2002) and ave. Sweden, 66 reports. [ available monthly quarterly available annual annual/ at (i) source (from ] 70 Failure reports (Finland, Denmark Germany Performance failure Online [ Failure reports Data Performance failure monthly Reports 1999 Type On On WTs On 1987 1997 1991 1989 2005 From Ongoing From Ongoing Period From Ongoing From to Denmark – Sweden Germany Country Finland databases/initiatives. Power the AB) Media (prev. of Power Wind 4 Newletter Haymarket Group ELFORSK Finnish Association Vattenfall Consultant Swedpower Windstats Vindstats Database/Source Organisation Felanalys VVT Table Cataloguing
6 D. Cevasco et al. Renewable and Sustainable Energy Reviews 136 (2021) 110414 , , ] 61 29 , ] ] ] , ] ] 7 71 8 [ 59 76 28 20 , [ , , ] ] , ] ] , ] ] 75 5 , ] , 34 65 26 73 27 72 78 15 26 2 77 [ 74 [ [ [ [ [ [ [ [ Det Ave 31 [ [ References (3 and WT to sys., wind and only group for into of all size per of drivetrain rep.) per size with for location unplanned failure and control λ results (per group (details sub-syst.) given area broken in age, of compared for gears, size age from survey sys., comp. per MDT and per rate result type (auto-manual distributions provided min./maj. per per compared or stops size Findings averaged categories and WMEP drivetrain WT electrical generators, Results Only Failure Information Periodicity Influence Results concepts installation Differentiate severity restart, MART Results - concept - maint. • hydr. o Results - cost - configuration for - speed - sites) Results of Time compared size Average component) the Weibull drivetrain Main collected data (t) T X X X X X A CF X X X X X KPI MART (%) X X MTTR MDT X X X MTTR X RT down time λ (t) (t) i i (t) (t) λ λ λ λ λ N(t) N λ λ λ (%) X Reliability/Performance λ DDE (by (by C, I all concept) X X X all concept) C DImP DI1P DImW X A0/A1/ A2 B, X A2 B C D X Type age (early 4 [time 15 5 10 – 2 ÷ ÷ ÷ ÷ X X X 1 1 1 X Average (t) X 1 fail. excluded) X Age scale] 3 in 1.5 1.5 1.5 1.75 rating ÷ 1 1 2.5 ÷ ÷ ÷ 1.5 1.5 Characteristics ÷ ÷ ÷ ÷ ÷ 0.5 1.0 0.5 1.0 < 0.5 ≥ 0 0.56 X < 0.5 ≥ 1.5 0.3 0.33 0 0.56 0.225 (divided groups) X 3.0 0.85 2.0 0.6 WT Power [MW] 32 in 23 4 36 2008): per 2000): 2004): A: B: C: ~1435 865 – – WTs: 400 (until (in WF WF WF E32/33 published 77 26 WTs (1999 (1994 in in in of turbine WTs WTs WTs WTs survey) ~1500 ◦ 2000): 2005): – – E32/33 years n ◦ ◦ ◦ ◦ 1500 653 (for n n n n – of WTs WTs WTs WTs WTs WTs: WFs: 1822 13 ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ N N Ave Ave over (1998 (2004 Ave 2006): C: DImP/DI1P: (~24% WMEP N 510 N 158 In ~5800 year N N N N Total Size results) Ave one data type about masts report raw for from and weather (v) (~64,000 (iv) annual (i) (i), (i), (iii.a/b) source from years Additional data meteorological Type Failure Type Type manufacturer Data Type 17 reports) (cont.1). On On On On WTs On to to to to to From ~1997 2006 From 1993 2006 From ~2010 2014 From ~2009 2014 Period From 1989 2008 databases/initiatives onshore UK Germany (Schleswig- Holstein) Netherlands UK Country Germany main the of of Gordon (prev. 5 Robert University Land Wirtschafts- Kammer MECAL Independent Experts (Onshore) University Strathclyde Fraunhofer IWES ISET) RGU LWK MECAL Strathclyde Database/Source Organisation WMEP Table Cataloguing
7 D. Cevasco et al. Renewable and Sustainable Energy Reviews 136 (2021) 110414 ] ] ] ] ] 82 79 81 4 [ [ [ [ ] ] ] 80 ] , ] 40 4 8 42 3 26 [ [ [ Det Info [ [ Det Info [ References of (in ] yaw the sys. brake brake and 2 [ values, on blades blades blade type impact controller stat., pitch blade blade Ref. ] in preventive size/type) the 2 failures to mech./electr. maint. [ (NA) absolute for of of on meteo. blades, by (per generator, generator, generator, blades, control, blades, drivetrain results information given systems/component logs analysis per Findings collection according corrective insights derived T gearbox, gearbox, generator, gearbox, gearbox, gearbox, controller, gearbox, Downtime Cost λ A given 2011/12) - system and - - - • o • o • sys. o Unpublished but single 2012/13 unavailability General data Comparison alarm • o Main collected data (t) (t) (t) O O T T A X X CF X A CF(t) A X X A KPI TTR TTR X % (%) MTD MTTR (t) X MART X down time NA λ ) λ (t) i (t) % X X λ ( λ λ % (%) λ Reliability/Performance λ in ]) 26 (info [ /X C DD C DD ✓ Ref. X A0 C Variable- speed C DImP DDP Type in (WTs 3 – 2 X X X X X X > ~2 installed 2008/2009) Age 9 top 2 0.6 rating 2 1 ÷ ÷ (only 3 Characteristics ÷ ÷ ÷ 0.85 /X 1 0.6 X 0.3 > 0.85 2 0.04 X 0.225 ✓ manufacturers) WT Power [MW] 15 10 WTs WTs: WFs: WTs WTs: WTs WTs DI DD WTs: WTs: MW: MW: 2012) s s s ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ 383 57 ’ ’ ’ (for n n 1 1 n n 215 111 560 640 2016) WFs: WTs: : : : < ≥ 4300 ◦ ◦ ◦ ◦ ◦ Ave.n (in Ave.n N 2010 n WTs: Ave.n WTs: 290 ~900 N (until < Ave 230 Ave C 2130 C 2270 DD: ~350 2011 n 2012 n Ave Ave.n Size published results) Ave 47 a/b), type raw raw 14 and (iii. (iii.a), and from (iii) (iii.a), from and and data data (iii.a/b), (i) (i) (ii), (ii), (iii.a/b), (i) source (v) data Type data Type results Performance failure manufacturers Type Type Type (iv), Type data Data Type raw manufacturers (cont.2). On On On On On WTs On to 2010 1986 2000 2008 2007 1987 2004 2012 2010 From to From to From to From to From Ongoing Period From ~2013 2016 databases/initiatives onshore USA (California) India China Europe USA Country Spain main the de of Union project Wind Power Islam National 6 Chinese Energy Association Electric Research Institute Noorul University European founded Sandia Laboratory Universidad Zaragoza CWEA EPRI Muppandal ReliaWind CREW Database/Source Organisation CIRCE Table Cataloguing
8 D. Cevasco et al. Renewable and Sustainable Energy Reviews 136 (2021) 110414 ] ] ] ] 85 ] , ] 19 47 46 57 [ 44 [ [ ] ] ] – ] ] , ] 43 , ] 58 39 5 74 55 41 6 44 10 44 [ [ [ [ Det Info [ [ [ [ [ [ Det Info References for 1 of per for ] rotor and one (per of some 85 Europe ● , early the the in terms for month 84 pitch, [ pitch/ to (for in the ] listed per average broken: 2 by 1 number from time [ sys., percentage λ categories WFs sys., availability sys. above/below CF repair: in 4 by given installed rate el. critical within name cost of for projects Findings round configuration compared derived control WFs estimated hydraulic WFs λ into per details Most 2009/13 ): collected blades, Availability year) MW Issues years Downtime component Technical 10 - - percentage failures ○ Failure - - - components - some - Updated λ CFs from Results monthly • Main data (t) (t) (t) (t) O T T T CF(t) X A X X A CF(t) X A CF(t) X A KPI MDT MDT % MART MTD X X MDT (%) X X X X down time λ (t) i (t) λ X X N X X X λ λ λ λ % X Reliability/Performance λ at ] 63 [ ]) 63 X X C Derivable from C A1 C DI1P (accessed [ X C DImW DImS Type ] 63 [ on on from 15 3 3 5 7 8 5 15 ]) WF) WF) ÷ ÷ ÷ ÷ ÷ ÷ ÷ ÷ 8 5 5 83 [ 1 1 (depending the 1 1 1 (depending the Derivable > 5 < 3 > 1 Age Ref. in WFs rating 5.0 3.0 3.6 4.0 3.0 7 8 (info power- Characteristics ÷ ÷ ÷ ÷ ÷ – – 4 – /X 1.5 2.0 2.0 3.0 2.0 3.0 1.5 3.0 Ave number 83 155 266 2.0 ✓ 0.3 WT Power [MW] 10 26 1313 phase phase ÷ results) 120 371 36 277 806 1045 ~350 19 5 in in WFs: WFs: WTs: ◦ ◦ ◦ (for n n n WTs WTs WTs: WTs: WTs: WTs: WTs: WTs: WFs: WTs: WFs: 61 47 250 ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ N Ave N N Ave N N N N 1: N 2: N (throughout 2016) N Ave > Size published N (iii.a/ data data data from and (ii) (ii), (v) (ii) (i), (i), (i), (iii) source manufacturer Type Performance Performance Performance Availability statistics Type Type b) Type one Data type Type (cont.3). Off On Off Off On Off Off Off WTs On to to to to to to to May) – years) 2011 2013 ~2004 2000 2011 ~1992 2007 2013 ~2011 2005 2007 2007 (Jan (5 – – ~ From 2007 2012 From 2012 From 2014 From To From 2009 From ongoing From 2016 From 2014 From 2011 Period 2009 2011 databases/initiatives onshore UK China Germany UK Worldwide Netherland UK UK Country China main the WF IWES of of New Hassan Company WFs WFs aan 1 2 Catapult (OWEZ) 7 Durham University Huadian Energy Fraunhofer Durham University Garrad Zee - ORE (Offshore) University Strathclyde University Nanjing Round Huadian Offshore-WMEP Round GH Egmond SPARTA Strathclyde Nanjing Database/Source Organisation Table Cataloguing
9 D. Cevasco et al. Renewable and Sustainable Energy Reviews 136 (2021) 110414
Table 8 availability of spare parts, vessels and personnel for the repair of RDS-PP® taxonomy adopted for system and sub-system added with numbered damaged subsystems. labels for the presentation of the results. With regard to the distribution of unforeseen failures in time, this RDS-PP® Acronyms Labels information is modelled from the reliability module based on the reli
MDA 1. Rotor System ability data from the literature. The input failure rates are grouped into MDA10 1a. Rotor Blades minor repair (mr), major repair (Mr) and major replacement (MR), ac MDA20 1b. Rotor Hub Unit cording to the material costs indicated by Carroll et al. in Ref. [6]. When MDA30 1c. Rotor Brake System this information is not provided, the downtime statistics are used for the – 1d. Pitch System classification of the failure rates, similarly to what is suggested by MDK 2. Drivetrain System MDK20 2a. Speed Conversion System Ref. [91] (cp. Table 9). When a failure occurs, the turbine status varies MDK30 2b. Brake System Drivetrain depending on the failure type. For mr, the turbine is assumed to continue MDL 3. Yaw System operating even after the failure detection, and the shutdown is only MDX 4. Central Hydraulic System assumed during the repair time. For Mr and MR, the turbine is stopped MDY 5. Control System after the detection of malfunctioning, going back to service only after MKA 6. Power Generation System MS 7. Transmission the system is restored. The time to failure associated with each failure MSE 7a. Converter System mode, for a particular subsystem i, is assumed to be distributed by an MST 7b. Generator Transformer System exponential probability density function f(t) (Eq. (1)) with parameter MUD 8. Nacelle λi,mode being the failure rate for subsystem i under a particular failure MUR 9. Common Cooling System CKJ10 10. Meteorological Measurement mode (i.e. mr, Mr, or MR).
UMD 11. Tower System λi,mode t f (t) = λ , e UMD10 11a. Tower i mode (1) UMD80 11b. Foundation System The cumulative distribution function is the probability of failure – 12. Others (PoF) of the subsystem according to the exponential reliability theory simulated, and a number of KPIs are produced, such as the time-based availability, power produced, and operating costs. Table 9 Criteria for the classification of the reliability databases in minor and major 3.3.2. Wind farm availability estimation repair and major replacements. For the calculation of the lifetime availability of a wind farm, both Classif. mr Mr MR planned and unplanned maintenance activities are considered. On the Material cost ≤1000 € 1000 € < cost ≤10,000 € >10,000 € one hand, scheduled maintenance happens at yearly intervals and is Downtime (onshore) ≤3 days 3 days < downtime ≤ 7 days >7 days performed for each subsystem of the turbines in the farm following Downtime (offshore) ≤7 days 7 days < downtime ≤ 15 days >15 days grouping and prioritisation. The downtimes are calculated based on the maintenance activity duration which is assumed to be fixed. On the other hand, unplanned maintenance downtime is sensitive to the
Fig. 2. Workflow of openO&M tool.
10 D. Cevasco et al. Renewable and Sustainable Energy Reviews 136 (2021) 110414 and is given in Eq. (2). The PoF of the whole wind turbine is the PoF of all (mentioned in Section 2) are presented as dimensional quantities, in subsystems considering all failure mode classifications, as explained failure frequency against time lost to restore the system after failure. Due further in Refs. [88]. to poor documentation, confidentiality reasons, or the lack of a stand