Wind Speed Data Collected at a Proposed Site for Wind Energy Development

A Statistical Analysis and Fuzzy Logic Approach in Assessing the Performance of Wind Turbine in Ohio

A Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

Mikail Suleiman, M.S.

Graduate Program in Civil Engineering

2010

Thesis Committee:

Fabian Hadipriono Tan, Advisor

William E. Wolfe

Qian Chen

Mikail Suleiman

2010

ABSTRACT

The global demand for petroleum products has caused the rise in their cost. In fact, the cost of petroleum products to generate electricity supply to meet the United

States’ energy demand is on a continuous rise. As a result, this rise in cost of petroleum products has challenged our nation to seek an alternative source of renewable energy generation. Wind energy generation is one of the fastest growing forms of electricity generation in the world today compared to other sources of renewable energy. According to the National Renewable Energy Laboratory, the United States currently generate more than 25,000 megawatts (MW) of electricity from the wind, which is enough to power electricity supply to almost 7 million homes, and experts in wind power development predict that, with proper development, wind energy could provide 20% of the nation's energy needs.

To perform an assessment for a feasible wind energy project and wind turbine performance, an empirical analysis using statistical models was performed on wind speed data collected at a proposed site for wind energy development. Because of the variability in wind speed and wind turbine location, a subjective description of wind turbine location using a fuzzy logic approach was used to define the two variables, and quantify the different components and elements of the wind turbine performance and wind turbine location, and subsequently to evaluate the total wind farm development project

ii performance using two forms of fuzzy member. Two software programs were developed using the concept of fuzzy logic, which transforms the linguistic expressions such as

“Low,” “Fairly Low,” “Medium,” “Fairly High” and “High,” into mathematical representations. The two fuzzy logic models created were the “angular model” and the

“triangular model,” which were used to complement the statistical models in assessing the wind turbine location and turbine performance.

The angular model and triangular model incorporate users’ subjective preferences and choices based on the information available to them. This study advances the assessment of a wind energy development project by harnessing the available wind resource to help meet the nation’s goal of providing 20% of the nation electricity demand

2030.

If properly implemented, wind energy development would help reduce the consumption of 4 trillion gallons of water and reduce CO2 emissions, reduce total natural gas consumption by 11%, reduce electric utility coal consumption by 18%, reduce electric utility natural gas consumption by 50% and avoid construction of 80 GW of new coal power plants through year 2030.

iii

DEDICATION

This research work is dedicated to my parent, Mr. Suleiman Aka’aba & Mrs. Halima Aka’aba

ACKNOWLEDGEMENTS

First and foremost, I would like to express my gratitude to Mrs. Dr. Tan for her

patience and understanding while Dr. Fabian Hadipriono Tan stayed late in his office to

work with his student.

My sincere appreciation to Dr. Fabian Hadipriono Tan for over the years, he has

dedicated himself to the student he has taught, mentored and shared his experiences and

above all providing me with the best advice and guidance, and he challenged and

extended me to a new academic heights. His patience and genuine concern had no

boundaries. Dr. Tan, …yes as always, you are simply the best and you were the best

advisor I could have ever hoped, prayed, and asked for, and to that end I am forever

grateful to you.

I would like to thank the Masters Examination committee members; Dr. William

Wolfe and Dr. Qian Chen for their valuable suggestions to this research work.

I would like to thank my parents, Mr. Suleiman Aka’aba and Mrs. Halima Aka’aba for

their continuous support, encouragement and constant prayers, and to all my siblings and

their spouse for their support and tireless prayers; Amina, Sadiya, Fatima, Zubair and

Hafsat… to you all, I say thank you.

To my wife Alisha for I may not have say “thank you” at all times, but your

support and patience during this research work and at all time are very well appreciated

and I say thank you for all your help and I love you.

This is to you my very good friend, Aous Al-Khalidi, whom I owe a thank you, for your

help and all incessant support and enlightening discussions during my graduate studies.

Thank you Emily Sautter, for all your help in guiding me through the review process of

the data sets collected for this research work and to you Charm Damon for editing this

research work.

Also, I would like to thank my family and friends for their much needed support,

Dr. Abdulshafi & Family, Brigadier Saka Abubakar, Dr. William Olorunto & Family,

Mr. Ben Bosah & Family, Mr. Val Mbah & Family, Mr. Abdirahim Abdi, and to all my friends not mentioned, but not forgotten, I thank you all for your friendship and support during my graduate studies!

To Mikail Zayd Aka’aba for you are the future greatest Engineer!

VITA

September 1964…………………………...Born-Sokoto, Nigeria 1990……………………………………….B.S. Civil Engineering, Kaduna Polytechnic, Kaduna Polytechnic, Kaduna

2003……………………………………….M.S. Administration, Central Michigan University, Mt. Pleasant, Michigan, U.S.A

2003 to present……………………………Member, American Military Engineers 2003 to present……………………………Member, American Society of Civil Engineers 2003 to present……………………………Member, Institute of Transportation Engineers 2003 to present……………………………Member, IEEE Computational Intelligence Society

2009 – 2010……………………………….Emmett Karrer Scholarship Award 2008 – 2010.………………………………Graduate Student, The Ohio State University

1997 – 2000….……………………………Field Engineer – Construction Padia Environmental, Inc. Worthington, Ohio

2000 to present……………………………Project Manager – Construction Inspection Division of Design & Construction City of Columbus, Columbus, Ohio

FIELDS OF STUDY

Major Field: Civil Engineering Specialization: Construction Engineering & Management

vii

TABLE OF CONTENTS

Page

Abstract….…………………………………………………………………………….ii

Dedication.…………………………………………………………………………….iv

Acknowledgements..…………………………………………………...... v

Vita..…………………………………………………………………………………..vii

List of Tables..………………………………………………………………………...xii

List of Figures.………………………………………………………………………..xiii

Chapter 1: Introduction.……………………………………………………………….1

1.1 Introduction .……………………………………………………....1

1.2 Objectives of the Study…………………………………………....3

1.3 Benefits of the Study ………………………………...... 4

1.4 Scope & Limitation…………………………………...... 5

1.5 Organization of the Study ………………………………………...6

Chapter 2: Literature Review………………………………………………………….7

2.1 Introduction………………………………………………………..7

2.1.1 Characteristics of Wind Resource & Atmospheric

Boundary Layer ……………………………………...10

2.2 Wind Data Collection & Measurement…………………………..17

2.2.1 Wind Measurement & Instrumentation…………………17

2.2.2 Wind Data Acquisition & Analysis..…………………...18

2.2.3 Raw Data Collection…………………………………....18 viii

2.2.4 Data Storage, Retrieval & Documentation…………….19

2.2.5 Data Validation……………………….………………..19

2.3 Statistical Analysis Studies………………………………………22

2.3.1 Statistical Data…………………………..……………. 22

2.3.2 Wind Data Analysis & Resource Estimation………….31

2.3.3 Structure of Wind Measuring System…..……………..33

2.3.4 Wind Power Cost and Financing………………………34

2.3.5 Wind Turbine Energy Production Estimate…………...38

2.3.6 Accuracy of Period for Return on Investment.………..39

2.4 Wind Speed Predictive Models..………………………………...41

2.5 Fuzzy Logic Studies..……………………………………………44

Chapter 3: Methodology of Studies.…………………………………………………47

3.1 Introduction..…………………………………………………….47

3.2 Statistical Analysis Studies..…………………………………….47

3.2.1 Wind Direction..………………………..……………...51

3.2.2 Cubic Wind Speed..………………..…..………………58

3.2.3 Wind Shear Analysis..…………………………………60

3.2.4 Wind Turbulent Intensity..…………………. ………...64

3.2.5 Air Density..…………………………………………...67

3.2.6 Wind Power Density..…………………..…………...... 69

3.2.7 Regression Fit Analysis and Trend Analysis..………...72

3.2.8 Wind Power Generation Analysis……………………..79

Chapter 4: Assessment of Wind Turbine System Using Angular Model……………87

4.1 Introduction……………………………………………...87

4.2 Approximate Reasoning…………………………………………90

4.3 Angular Fuzzy Set Model..………………………………………92

4.3.1 TFM Using Angular Model…………………………....92

4.3.2 ITFM Using Angular Model…………………………...96

4.3.3 Fuzzy Modus Ponens Deduction……………………………….99

4.4 Illustration of Angular Model…………………………………...104

Chapter 5: Assessment of Wind Turbine System Using Triangular Model.………..110

5.1 Introduction...…………………………………………………...110

5.2 The Triangular Fuzzy Set Model………………………………..111

5.3 Mamdani Inference System Approach………………………….112

5.3.1 The Fuzzification Unit………………………………...113

5.3.2 The Fuzzy Rule Base Unit…………………………….114

5.3.3 Fuzzy Inference Engine Unit………………………….114

5.3.4 The Defuzzification Unit……………………………...117

5.3.4.1 The Centroid Method………………………..117

5.3.4.2 The Weighted Average Method……………..118

5.3.4.3 The Mean Max Membership Method.………123

5.3.4.4 The Max Membership Method.……………..124

5.3.4.5 The Center of Sums Method.………………..125

5.3.4.6 The Center of Largest Area Method.………..128

5.3.4.7 The First (or Last) of Maxima Method………129

5.4 Illustration of Triangular Model and Mamdani Approach………131

5.4.1 Using the Triangular Model……………………………133

5.4.2 Examples to Demonstrate the Use of the Model……....134

Chapter 6: Wind Energy Systems: Environmental Aspects & Impacts.…………….138

6.1 Introduction.……………………………………………………..138

Visual Impacts of Wind Turbines.……………………………...... 140

6.3 Avian Interaction with Wind Turbines.……………………….....141

6.4 Impacts of Wind Turbine Noise.………………………………...144

6.5 Land Use Impacts of Wind Power System………………………………147

Chapter 7: Summary, Conclusion & Recommendation..…………………………....149

7.1 Summary…………………………………………………………149

7.2 Conclusion……………………………………………………….151

7.3 Recommendation/Future Study..………………………………...153

List of References…………………………………………………………………….155

Appendices……………….………………………………………………………….158

Appendix A: Incident Log..………………………………………………….158

LIST OF TABLES

Table Page

2.1 NREL Wind Power Density Classification………………………………….....13

2.2 Estimate of Wind Power Cost.…………………………………………………37

2.3 Influence of the Accuracy of Wind Speed Data on ROI……………………….40

3.1 Summary of Statistics -Toledo Site …………………....………………………50

3.2 Average Wind Shear Data Measurement Recorded at Other Site Location...... 64

3.3 Average Wind Power Density for Toledo Site.………………………...... 69

3.4 Summary of Turbine Energy Output and Capacity Factor for 12 month…...... 80

4.1 Linguistic Expressions and their corresponding Angular Values.……………..88

4.2 NREL Wind Classification and Corresponding Linguistic Expressions……..105

4.3 NREL Wind Classification ……………………………..…………………….105

5.1 Inference Rules with Relationship.…………………………………………...115

5.2 Use of Wind Speed Linguistic Expression for Triangular Model..…………..132

5.3 Use of Turbine Location Linguistic Expression for Triangular Model..……..132

xii

LIST OF FIGURES

Figures Page

2.1 Wind Turbine Engine.………………....…………………………………….....8

2.2 World Wind Power Capacity Growth..……………………………………….10

2.3 Ohio Annual Average Wind Power…..……………………………………....14

2.4 50m Wind Map of the State of Ohio…………………………………………15

2.5 Data Validation Flow Chart……………………………………………….....21

2.6 U.S. National Wind Project Growth ...………………………………………23

2.7 U.S. Sources of Electricity Generation……………………………………...24

2.8 U.S. Sources of Renewable Electricity Generation………………………....25

2.9 Wind Power Capacity Installation by States in U.S………………………...26

2.10 Photo of 1.8MW turbine installed at Bowling Green, Ohio.………………..27

2.11 Photo of 1.8MW turbine installed at Bowling Green, Ohio.……….……….28

2.12 Power Output Display Panel of Vestas 1.8MW Turbine installed at

Bowling Green, Ohio.……………………………………………………….29

2.13 View of the Tower Base of 1.8MW Turbine installed at Bowling Green,

Ohio……..…………………………………………………………………..30

2.14 Two of the four 1.8MW wind turbine at Bowling Green, Ohio……...... 31

2.15 Illustration of Error in Calculation of Annual Energy Production..………..38

3.1 Aerial View of the Toledo Site……………………………………...... 48

3.2 A Wind Rose Plot of Wind Direction for Toledo Site at 79m...……………52

3.3 A Wind Rose Plot of Wind Direction for Toledo Site at 61m.……………..53

xiii

3.4 A Wind Rose Plot of Wind Direction for Toledo Site at 43m……………...54

3.5 Distribution for Monthly Average Wind Speed at 79m……………...... 56

3.6 Distribution for Monthly Average Wind Speed at 61m..……………….….56

3.7 Distribution for Monthly Average Wind Speed at 43m..…………………..57

3.8 Illustration of Monthly Average Wind Speed……………………………...58

3.9 Illustration of Monthly Average Cubic Wind Speed……………………….60

3.10 Illustration of Monthly Average Wind Shear……………………...... 63

3.11 Photo of an NRG-Data Logger……………………………………………..65

3.12 Illustration of Monthly Average Turbulent Intensity.……………………..66

3.13 Illustration (Scatter-plot) of Turbulent Intensities & Wind Speed...………67

3.15 Illustration of Monthly Average Wind Power Density...………………….70

3.16 Comparison of Monthly Average Wind Speed……………………………71

3.17 Comparison of Monthly Average Wind Power Density..…………………72

3.18 Toledo Site Distance from Reference Station..……………………………73

3.19 Comparison of Wind Speed Probability Plot..…………………………….74

3.20 Comparison of Time Series Analysis of Average Wind Speed..………….75

3.21 Scatter plot Analysis of Average Wind Speed..…………………...... 76

3.22 Regression Analysis of Average Wind Speed..…………………………...77

3.23 Summary of Residual Plot Analysis of Average Wind Speed.……...... 78

3.24 Type-A Turbine 12 Months Projected Energy Output...……….…………..81

3.25 Type-B Turbine 12 Months Projected Energy Output....….…………….....81

3.26 Type-A Wind Turbine Power Curve...……………………...………………83

3.27 Type-B Wind Turbine Power Curve………………………..………………84

xiv

3.28 Type-C Wind Turbine Power Curve………………………..………………85

3.29 Turbine Manufacturer’s Installation..…………………….. ..………………86

4.1 Angular Fuzzy Set Model...…………………………………………………90

4.2 Angular Model………………………………………………………………94

4.3 Angular Model………………………………………………………………97

4.4 Wind Turbine Illustration Model....……………………………………….106

4.5 Wind Turbine Illustration Model – Example 1.……………………...... 107

4.6 Wind Turbine Illustration Model – Example 2.……………………...... 108

4.7 Wind Turbine Illustration Model– Example 3.…………………………….109

5.1 Illustration of Triangular Numerical Values.…………………….…...... 111

5.2 General Fuzzy Controller Chart.……………………………………..……113

5.4 Max-of-Mins Method for 2 rules.………………………………………….116

5.5 The Centroid Method.……………………………………………………..118

5.6 The Centroid of Membership Function A.…………………………...... 119

5.7 The Centroid of Membership Function B…………………………………120

5.8 The Centroid of Membership Function C…………………………………121

5.9 The Overall Weighted Average Value…………………………………….122

5.10 The Mean-Max Defuzzification Method………………………………….123

5.11 The Max Membership Defuzzification Method…………………………..124

5.12 The Center of Sums - Membership Function A.………………………….126

5.13 The Center of Sums - Membership Function B...………………………...127

5.14 The Center of Sums Defuzzification Method - Total Overall Value……..128

.5.15 The Center of Largest Area Method………………………………………129

5.16 The First and Last of Maxima Method……………………………………130

5.17 The Triangular Model – Example 1...…………………………………….134

5.18 The Triangular Model – Example 2....……………………………………135

5.19 The Triangular Model – Example 3………………………………………136

5.20 The Triangular Model – Example 4………………………………………137

6.1 The Primary Routes taken by Migratory Birds...…………………………143

6.2 Decibel Level of Some Common Noise..…………………………………145

xvi

CHAPTER 1

INTRODUCTION

1.1 Introduction

For over a century, our world has been powered primarily by carbon fuels. Today, concerns about global warming and the effects of fuel emissions have created new demands for cleaner and sustainable energy sources like wind. Wind is an important source of homegrown American energy and with soaring energy prices and global climate change, this clean natural resource holds more promise today than ever before.

The development of wind energy resources would advance the economy and provide

clean energy that could help make the nation more energy independent and efficient,

which would help increase incomes, create jobs and help the economy for a potential

national cap on global warming and pollution.

According to Environment Ohio (August, 2007), diversifying into wind energy, will

help create more jobs in Ohio up to 20% by the year 2020, and an estimated net of

$40,000 per person per year of employment through 2020 will be created in the state of

Ohio. Also, it would help increase the gross state product (GSP) by an estimated $8.2 billion through 2020.

An economic model was developed by the Renewable Energy Policy Project and

Policy Matters, Ohio to evaluate the impact of increasing wind energy production to 20% on Ohio retail electricity sales by year 2020. The model estimated that more than 13,000 jobs would be created in wind turbine component manufacturing jobs in Ohio. The number of jobs created in Ohio would be more than every state in the United States, except California.

In addition, harnessing wind resources would help rural areas benefit from wind energy with landowners leasing out their land to developers of wind energy development.

It is estimated that Ohio’s use of wind energy could supplement landowner income with cumulative total lease payments of up to $200 million through 2020. Furthermore, developing wind energy would generate up to $1.5 billion in property taxes through 2020 to fund education and other local government services, such as recreational facilities, improvement of local library facilities etc.

In this research work, wind data were collected from a proposed wind farm development site using meteorological instrumentation. These data were analyzed using statistical analyses and models. In addition, the concept of fuzzy logic was adapted by developing fuzzy logic models to assess the effectiveness wind turbine performance and wind farm development.

The use of the fuzzy set approach involves individual subjective assessment using qualitative data, which include some of the following linguistic terms: “very good”,

“good”, “fair” and “poor.” A fuzzy set approach is then used to transform these linguistics terminologies into mathematical expressions.

1.2 Objectives of the Study

The research objective was to develop a study on harnessing and developing

Ohio’s wind energy resources to help make Ohio more energy independent and efficient,

which would help increase incomes, create jobs and help the general economy in Ohio.

The researcher used statistical analysis and fuzzy logic models to assess the effectiveness of a wind farm project development and construction in areas where there could be potential adequate wind resources in the state. The particular area studied was in

Toledo, Ohio.

The goal of this research would be achieved by consultation with management and staff of governmental agencies such as National Renewable Energy Laboratory

(NREL) of the U.S. Department of Energy, Green Energy Ohio (GEO) and National

Climatic Data Center (NCDC) for meteorological information and data collection. The data collected were used for statistical analyses, forecast prediction models, and assessment of wind turbine performance.

After analyzing the data collected at the proposed site, and if there is evidence of sufficient wind resources, we believe that the Toledo Zoo and residents in the vicinity would benefit tremendously if wind power energy is properly harnessed and developed for electrical energy generation.

1.3 Benefits of the Study

The average Ohioans emitted approximately 11.5 tons of CO2 from electricity

consumption in 2004 and the state of Ohio is ranked 19th per capita in CO2 emissions

from electricity generation and usage. More than 90% of that power is generated by

burning fossil fuels (coal and natural gas). These fuels produce CO2 during the

combustion process, so that according to a 2008 NREL report, the average Ohio resident

emitted approximately 11.5 tons of CO2 from electricity consumption.

Some of the benefits of wind power system development in Ohio will result in

CO2 emissions reductions, which would be less than electric power generated from coal plants and natural gas consumption, since fossil and nuclear-based electricity generation consume large amounts of fossil fuels for production of electric energy.

A report from NREL (2008) “forecasts the cumulative economic benefits from installation of 1000 MW of wind power development in Ohio to be $1.3 billion, annual

CO2 reductions of 2.5 million tons and annual water use savings of 1,343 million

gallons”. This investment will generate substantial direct and indirect economic benefits

for Ohio. Direct benefits include jobs, land-lease payments, and increased tax revenues

generated from property taxes by investors of wind farm. Indirect benefits would include

benefits to businesses that support the wind farm installation.

In this study, the researcher used statistical analysis and a software program, the

C# (C-sharp) programming language to develop fuzzy logic models to help assess the

effectiveness of selecting a location and determining the maximum output performance

of a wind farm turbine development.

The users of the models (e.g., design engineers and project engineers) could use

these models to select a suitable location and assess the effectiveness and performance of

a turbine. As mentioned earlier, a suitable location of wind farm project and wind

resource availability can boost the effectiveness of a wind turbine output performance, resulting in wind energy resources, which could help make Ohio more energy independent and help create more jobs up to 20% by year 2020.

1.4 Scope and Limitation

This research work is limited to a pre-selected location where wind speed data were collected using anemometer installed at three different height levels on the Lucas

County Emergency Management System (EMS) communications tower in Toledo, Ohio.

Statistical data analyses using time series, regression and fuzzy logic models were performed to create wind prediction models and to assess wind turbine performance at this location.

Moreover, the task in developing fuzzy logic models includes the application of two different fuzzy logic models, i.e., the Angular Model and Triangular Model using a

C# (C-sharp) programming language. While this study is limited to a pre-selected

proposed wind turbine location in Toledo, Ohio, users could use this work as a model for making selections on wind turbine performance based on the information available to them for different locations.

1.5 Organization of the Study

The statistical analyses were based on the wind data collected from the proposed site using meteorological instrumentation to assess wind speed resource, wind turbine performance and to perform wind speed forecast analysis.

The researcher would like to emphasize that the fuzzy logic models in this research work were not used to compare with the statistical analysis, but rather to complement it. The researcher made a subjective selection of linguistic expressions for the wind turbine location and used a corresponding wind speed class from the National

Renewable Energy laboratory (NREL) wind classification table to assess wind turbine performance.

CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

According to Blanchard and Desrochers (1984), the first statistical studies of wind

speed as a discrete random variable began 50 years ago. Over this period, different

distribution functions have been suggested to represent wind speed including those of

Pearson, Raleigh and Weibull analysis, Blanchard and Desrochers (1984), Chou and

Cortis et al (1978).

Several previous studies, for example those of Chou and Cortis (1981) and

Blanchard and Desrochers, (1984) have attempted to incorporate autocorrelation into

wind speed models using the methodology of time series analysis developed by Box and

Jenkins (1976) to represent the fluctuations of hourly wind speed data. Thus, Blanchard

has fitted the observed hourly wind speed data by using the stochastic model directly.

A wind turbine is a machine that converts the power in the wind into electricity.

This is in contrast to a ‘windmill,’ which is a machine that converts the wind’s power into

mechanical power. This mechanical power can be used for specific tasks such as grinding

grain or pumping water. A wind turbine, on the other hand, converts the kinetic energy

in the wind into a power generator that can convert this mechanical power to electricity.

The modern wind turbines that are commonly used today fall into two basic groups;

1. The horizontal-axis and,

2. The vertical-axis design, which is similar to the eggbeater-style (the

Darrieus Model), named after its French inventor.

The horizontal-axis wind turbines typically have either two or three blades. These three- bladed wind turbines are operated "upwind," with the blades facing into the wind. As

electricity generators, wind turbines are connected to an electrical network. These

networks may include battery charging circuits, residential scale power systems, isolated

or island networks and large utility grids. Figure 2.1 is an illustration of a typical

architecture of a modern wind turbine engine.

Figure 2.1: A Typical Diagram of Wind Turbine Engine. Source:www1.eere.energy.gov/windandhydro/pdfs/FY08 (Used with permission for educational purpose)

The use of windmills to generate electricity is a twentieth-century development, which initially developed at low power levels for uses such as battery charging. During the first oil crisis in the early 1970s, the rise in oil and petroleum products prices shocked the world and thereby inspired interest in alternative energy sources.

In the last decade, research and development into modern wind turbine technology received a considerable boost in several countries including the United States, and the increasing concern for the environment provided a further drive for cleaner sources of energy, which includes wind energy. The increase in oil products and concerns for the environment promoted interest in the large-scale generation of electricity using wind power, until 1996 when approximately 6,000 MW (megawatts) of wind plant were installed in countries throughout the world.

According to the American Wind Energy Association (AWEA 2008), over 8,500

MW of wind power has been installed in the U.S. by the year 2008, which is up from over 5,200 MW installed in 2007. This provided 42% of all the new generating capacity added in the United States. The new projects installed in 2008 represent an investment of

$17 billion, which is the largest capital investment in the U.S. electricity sector in that year according to the report.

Figure 2.2: World Wind Power Capacity Growth, Source: AWEA Report 2008, (Use with permission for educational purposes)

In terms of total generating capacity, the turbines that make up the majority of the capacity are usually in the range of 500 kW to 2 MW or more, and these larger turbines are used primarily in large utility grids, which are found mostly in European countries and the United States.

2.1.1 Characteristics of Wind Resource & Atmospheric Boundary Layer

The energy available in wind varies as the cube of wind speed. Therefore, an understanding of the characteristics of wind resource is critical to all aspects of wind energy utilization starting from the selection of a suitable site and predictions of the economic viability of a wind farm project, through to the design and installation of wind

turbines. Understanding wind turbine effect on electricity distribution networks and power generation supply to the consumers is also very important. The most important characteristic of wind as a resource is its variability. Wind is very highly variable geographically and this persists over a very wide range of scales, both in space and time.

The importance of this variability in wind is amplified by its cubic relationship to available energy.

On a large scale, the variability in wind speed describes that there are many different climatic regions in the world, with some areas having more wind than others.

Some of these regions are largely controlled by the latitude in which they are located.

There is an enormous difference in wind speed on a smaller scale, which is largely controlled by the physical geography of the area, the proportion of land and sea, size of land and the presence of mountains or plains in an area. The type of vegetation in the area may also have a significant influence through its effects on the absorption or reflection of solar radiation that is affecting surface temperatures and humidity.

According to Manwell et.al, winds are normally caused by atmospheric pressure differences across the earth’s surface due to uneven heating of the earth by solar radiation. The amount of solar radiation that is absorbed at the earth’s surface is greater at the equator than at the pole. In a simple air flow model, as air rises at the equator and sinks at the poles, the circulation of the atmosphere that results from uneven heating is greatly influenced by the effects of the rotation of the earth. The mechanics of the four atmosphere’s wind motion can be categorized into: (1) pressure forces, (2) coriolis forces,

(3) inertial forces and (4) frictional forces.

According to Energy Resource Information contained in the Wind Energy

Resource Atlas of the U.S. (1986), the U.S. is divided into grid cells of latitude by

longitude and each grid cell is assigned a wind power class ranging from 2 to 8, with 8

having the most wind. A typical class 3 wind resource for example can be found along

the exposed coastal areas from Delaware to North Carolina, Ohio and much of the

Midwestern states.

Additional information provided in the atlas are U.S. areas that are potentially suitable for wind energy applications (wind power class 3 and above), which are dispersed throughout much of the United States. The major areas of the U.S. that have a potentially suitable wind energy resource include the Great Plains from northwestern

Texas and eastern New Mexico northward to Montana, North Dakota, and western

Minnesota. Also, included are the Atlantic coast from North Carolina to Maine,

California to Washington and the Texas Gulf coast. The degree of certainty with which the wind power class can be specified depends on three factors:

1. The abundance and quality of data,

2. The complexity of the terrain and

3. The geographical variability of the resource

Because the wind power class values shown on the wind resource maps apply only to areas well exposed to the wind, the map area does not indicate the true land area experiencing this power. The fraction of the land area represented by the wind power class shown on the maps depends on the physical characteristics of the land-surface form.

For example, on a flat open plain, approximately 100% of the area will have a similar wind power class, while in hilly and mountainous areas the wind power class will only apply to a small proportion of the area that is well exposed to the wind.

The wind power density limits for each classification is tabulated in Table 2.1.

10m (33 ft) 50m (164 ft)

Wind Power Wind Power Wind Speed, Wind Power Wind Speed Class Density m/s (mph) Density m/s (mph) (Watts/ m2 ) (Watts/ m2)

2 100 4.1 (9.8) 200 5.6 (12.5)

3 150 5.1 (11.5) 300 6.4 (14.3)

4 200 5.6 (12.5) 400 7.0 (15.7)

5 250 6.0 (13.4) 500 7.5 (16.8)

6 300 6.4 (14.3) 600 8.0 (17.9)

7 400 7.0 (15.7) 800 8.8 (19.7)

8 1000 9.4 (21.1) 2000 11.9 (26.6)

Table 2.1: NREL Wind Power Density Classification Source: http:\\rredc.nrel.gov/wind/pubs/atlas/tables/A-8T.html (Use with permission for educational purposes)

The Ohio Annual Average Wind Power map in (Figure 2.3) shows the average wind power resources in Ohio with the Wind Power Classification Numbers on the map.

The numbers indicate the wind speed resource for an area based on the National

Renewable Energy Laboratory (NREL) wind power classification. As shown on the map, most part of the northeast to northwest area and extending from Sandusky towards 13

Toledo and up in the Lake Erie, Ohio has Wind Power Class No. 3 to 4, the highest wind resource in Ohio.

Figure 2.3: Ohio Annual Average Wind Power; Source: Wind Energy Resource Atlas of the United States, http://rredc.nrel.gov, (Use with permission for educational purposes)

Recently, the (NREL) published a new wind resource map for the state of Ohio, (Figure

2.4). This resource map shows wind speed estimates at 50 m above the ground.

Figure 2.4: A 50m Wind Map of the State of Ohio. Source: U.S. Department of Energy, (Use with permission for educational purposes)

The design and implementation of a proposed wind turbine project layout is a very important, challenging, and costly task. The wind direction and the turbine’s 15

interference with one another within the same vicinity require sophisticated modeling to

ensure maximum turbine output and efficiency. Typically, an average wind energy

project could cost approximately $1.2m to $1.5m per (MW) to be fully installed and

operational.

The wind data collected during the study were used to correlate with the wind

data from a local monitoring station, like a local airport or the meteorological data center.

The wind data were for the Toledo Express Airport, which was obtained from the

National Climatic Data Center (NCDC) that has wind data in the area for more than 10

years. According to AWEA, the following are typical ten (10) steps to building a wind farm:

1. Understand wind resources

2. Determine proximity to existing transmission lines

3. Secure access to land

4. Establish access to capital funding

5. Identify reliable power purchaser or market

6. Address siting and project feasibility considerations

7. Understand wind energy's economics

8. Obtain zoning and permitting expertise

9. Establish dialogue with turbine manufacturers and project developers

10. Secure agreement to meet operation and maintenance needs

The basic steps for a wind energy project construction include the following:

1. Initial access road construction to site

2. Construction of substations and turbine foundation

3. Electrical infrastructure and collection system

4. Turbine installation

5. Startup and project commissioning

2.2 Wind Data Collection and Measurement

2.2.1 Wind Measurement and Instrumentation

Typically, before construction of a wind project, the wind must be

measured for a minimum of one (1) year by installing a wind monitoring device at a minimum of two-third (2/3) the height of the turbine being considered for installation to capture seasonal differences in the area.

The wind data collected will then need to be correlated to wind data from a local monitoring station that has wind data in the area for approximately 10 to 20 years, (e.g., local airport and meteorological station).

It is important to note the three types of instrument systems used for wind measurements:

1. Instruments used by national meteorological services

2. Instruments designed specifically for measuring and characterizing wind

resource

3. Instruments specifically designed for high sampling rates for determining

wind gust, turbulence, and inflow wind information for analyzing wind

turbine response

For wind energy design and applications, the following types of meteorological

instruments are used:

1. Anemometers - to measure wind velocity

2. Wind vanes - to measure wind direction

3. Thermometers - to measure the ambient air temperature

4. Barometers - to measure the air pressure

The “instruments designed specifically for measuring and characterizing the wind resource” as stated in item 3 above of instrument system were considered for use for the purpose of this research work.

2.2.2 Wind Data Acquisition and Analysis

The important aspects of wind data acquisition and analysis include an on-site data storage (raw data), data retrieval and frequency, data protection and documentation.

To effectively perform these tasks, wind data collection and handling from the monitoring device system would need to incorporate procedures that offer a high level of data protection. These data acquisition procedures should comply with those specified by the data logger manufacturer.

2.2.3 Raw Data Collection

Raw data are data that have not been subjected to any kind of validation or verification process, and are typically stored by the data logger in a binary format. It is usually recommended that the storage device should be non-volatile, such as a read-only

memory disk, flash memory cards, floppy disks, optical discs and magnetic tapes so

that the ability to store data is not affected by loss of an electricity power system.

2.2.4 Data Storage, Retrieval and Documentation

During the process of data storage and documentation, several types of data

storage system and corresponding data transfer methods are used. Some of these data

storage types are; Solid State Devices, Data Card, Magnetic Media, Data Chip and

Portable Computer. These devices are usually used to read and transfer data at the central

computer station using software provided by the manufacturer.

During the implementation of this process, it is usually recommended that a detailed database be maintained and a Site Data File Log be developed to serve as the

Master Raw Data File Log for the proposed site.

Also, with the selection of all relevant information, the log’s function can be extended to allow data monitoring personnel to track the success of data transfers, especially for those that use telecommunication technology such as email, portable drives and file backup devices.

In addition to the organizational benefits, this documentation provides valuable

and immediate quality control feedback on equipment performance and data

completeness.

2.2.5 Data Validation

Data validation is defined as the check for all the collected data for completeness

and the elimination of spurious values to a reasonable level. This step transforms raw

19 data into validated data, which is a process used to produce the summary reports that are required for further analysis.

Data are usually validated either manually or automatically using computer- machines. Computer-based machines are usually preferred to take advantage of the power and speed of the technology; however, manual reviews are sometimes required to ensure proper validation.

The process of using computer-based technology for data validation process would require the purchase of “validation software” such as Microsoft Excel, Access or similar spreadsheet software from data logger manufacturers and vendors of such spreadsheets, databases and statistical software. The advantage of using spreadsheet programs is that they can also be used to process data and generate reports.

The wind data collected and validated will then need to be correlated to wind data from a local monitoring station that has wind data in the area for approximately 10 to 20 years, (e.g., local airport and meteorological station), as earlier mentioned.

A typical data validation flow chart is illustrated in Figure 2.5;

Data Validation Flow Chart

Raw Data Files

Develop Data Validation Routines General System and Measured Parameter Checks

• Range tests • Relational tests • Trend tests • Fine-tune Routines with Experience

Validate Data • Subject all data to validation

• Print validation report of suspect values • Manually reconcile suspect values • Insert validation codes • Alert site operator to suspected measurement

problems

Create Valid Data Files

Process Data and Generate Reports

Figure 2.5: Data Validation Flow Chart; Source: Wind Resource Assessment Handbook, 1997 (Use with permission for educational purposes)

2.3 Statistical Analysis Studies

2.3.1 Statistical Data

According to American Wind Energy Association, annual report (AWEA, 2008), the U.S. wind energy industry brought online for electric grid network more than 8,500

MW of new wind power capacity in 2008, increasing the nation’s cumulative total by

50% to over 25,369 MW. The new installations place the U.S. on a path in progress to

generate 20% of the nation’s electricity by year 2030 from wind energy, as long as the

industry continues to gain long-term policy support from the government.

The growth in 2008, as documented in the report, has seen an investment of about

$17 billion into the economy, making wind power as one of the leading sources of new

power generation in the country along with natural gas. The new wind project completed

in 2008 account for about 42% of the entire new power-producing capacity added

nationally during the year, and according to initial estimates this will avoid nearly 44

million tons of carbon emissions, which is estimated to an equivalent of taking over 7

million cars off the road.

The amount of energy that the wind industry brought online in grid network in the

4th quarter of 2008 is about 4,313 MW, which exceeds the annual additions for every

year except in 2007. In all, wind power generating capacity in the U.S. now stands at

25,369 MW, producing enough electricity to power the equivalent of close to 7 million

households in the U.S. and strengthening our national electric supply with a clean, source

of energy. Figure 2.6, is an illustration of cumulative wind energy growth in U.S. through

year 2008.

Figure 2.6: U.S. National Wind Project Growth, Source: AWEA Report 2008, (Use with permission for educational purposes)

AWEA (2008) reported that wind farm projects that were installed through the end of 2008 generated over 1.25% of the nation’s electricity in 2008. The report also documented nearly 4,000 MW of projects that could have been commissioned in 2008

were now carried over and brought online in the early part of 2009, and AWEA expects

over 5,000 MW of new capacity to be commissioned in 2009. (There has not been an updated annual report data for 2009 at the time this research work was completed).

The wind industry projects a significant growth within the next five years, for a

total installed capacity of 1,700 MW, due primarily to the new 30% federal Investment

Tax Credit passed by Congress in October 2008 and to be implemented in February 2009.

Similarly, the past five years have seen some annual growth in the wind industry in the

U.S. by an average of 22%. In 2006, America’s wind power generating capacity increased by 27%. The U.S. wind energy industry invested approximately $4 billion to build 2,454 MW of new generating capacity in 2006, making wind the second largest source of new power generation in the nation, which is surpassed only by natural gas for the second year in a row. The source of electric generation in U.S. is illustrated in Figure

2.7, and wind energy generation is categorized as renewable energy.

Sources of U.S. Electricity Petroleum &Petroleum Coke Renewables 1.1% Category Hydro 3.0% Natural Gas 6.1% Natural Gas Nuclear 21.6% Coal Hydro Renewables Petroleum &Petroleum Coke

Nuclear 19.7% Coal 48.5%

Figure 2.7: U.S. Sources of Electricity Generation; Source: AWEA Report 2008, (Use with permission for educational purposes)

Sources of U.S. Renewable Electricity

Solar Category Other BioMass 0.7% 13.8% Wind Geothermal Wood Other BioMass Solar

Wind 42.0%

Wood 31.4%

Geothermal 12.1%

Figure 2.8: U.S. Sources of Renewable Electricity Generation; Source: AWEA Report 2008, (Use with permission for educational purposes)

Recently, installed wind farms have increased the cumulative installed wind energy capacity in U.S. to 13,884 MW, which is well above the 10,000 MW goals that were reached in August 2006 (AWEA, 2007). On average, 1 MW of wind power produces enough electricity to power 250 to 300 U.S. homes.

Figure 2.9: Wind Power Capacity Installation by States in U.S, 2008 Source: AWEA Report 2008, (Use with permission for educational purposes)

Based on estimates released by the U.S. Department of Energy (DOE), Energy

Information Administration (EIA 2006), electric consumption in the United States is expected to grow at a rate of 1.3% annually, from 3.899 billion megawatt-hours (MWh) in 2006 to about 5.368 billion MWh in 2030. Hence, even if wind energy supplied approximately 0.8% of the total electricity in 2006, more and larger wind turbines can help to meet a growing demand for electricity.

During this research work, the researcher travelled to Bowling Green, Ohio, where four (4 no), 1.8 MW each of wind turbines were installed as the first utility-scale wind turbines in Ohio. Figures 2.10 and 2.11 are sky view photos of the wind turbine

installed at Bowling Green, Ohio. Figure 2.12 illustrates the power generating output

display panel installed inside the base of the turbine steel tower, and Figure 2.13 illustrates an access door into the tower for turbine maintenance.

Figure 2.10: Photo of 1.8MW Wind Turbine installed in Bowling Green, Ohio, Source: Personal Photo Collection, August, 2009,

Figure 2.11: A sky view of 1.8MW wind turbine installed in Bowling Green, Ohio; Source: Personal Photo Collection, August, 2009,

Figure 2.12: Power Output Display Panel of 1.8MW Wind Turbine installed in Bowling Green, Ohio Source: Personal Photo Collection, August, 2009.

Figure 2.13: Exterior view/entrance into Tower Base of 1.8MW Wind Turbine installed at Bowling Green, Ohio. Source: Personal Photo Collection, August, 2009

Figure 2.14: Two of the Four, 1.8MW Wind Turbine installed at Bowling Green, Ohio, Source: Personal Photo Collection, August, 2009

2.3.2 Wind Data Analysis & Resource Estimation

Since wind energy has become one of the most important sources of clean energy,

building accurate models for predicting power output and healthy monitoring of wind

farms is needed by this new wind industry. Developing such models is a challenging task,

since a large number of parameters are involved. Some of these parameters include

meteorological data collection, application of statistical modeling and fuzzy logic

modeling analysis.

Manwell et al. (2002), suggest that with wind resource data and an estimate of the

real efficiency of actual wind turbines, several investigators have been able to make estimates of the wind power or energy potential of regions of the earth and of the entire 31 earth itself. Goh et al. (2004) once proposed a neural network architecture, which is a complex-valued pipelined recurrent neural network (CPRNN) using a complex value that combined wind speed and direction into one complex value as input for predicting the turbine output inference system to forecast a wind time series.

Since then researchers have applied different methodologies in studying wind farms. Cameron and Michael (2006) combined the fuzzy set and neural network approaches in an adaptive-neurons-fuzzy inference system to forecast a wind time series.

The combination of these models was to make future prediction of wind speed for proposed wind turbine location. Landberg (1998) built a model to predict the power produced by a wind farm using the data from the weather prediction model (HIRLAM) and the local weather model (WAsP). Power prediction models are used to determine the rated power capacity based on the availability of wind resource in a proposed wind farm location.

Li et al. compared a regression and neural network (NN) models in order to estimate a turbine’s power curve and they reported that the NN model outperformed the regression model. The comparison of these models was to help determine the most effective models to perform time series analysis and an estimated turbine’s power curve.

According to Emden (2009), before an investment in wind turbines takes place, a feasibility study will need to be carried out, which gives information to potential investors about costs and benefits of potential energy production of a planned wind energy project.

The period in which the Return on Investment (ROI) has been reached is a very important figure for investors to pay attention to; therefore, the period on this ROI should

be well planned and determined accurately. An ROI would have been reached when the

total profits are equal to the investment made on a wind farm developed.

In this study, the researcher would like to emphasize that accuracy of wind speed

data is a very important factor in determining accuracy of the period for ROI. An accurate

wind speed data can save a lot of money when properly planned, as wrong decisions

about investment in inaccurate wind data can cause less production in wind turbines.

To obtain the required accuracy of the wind speed, it is necessary to use a

scientific calibration procedure on all the meteorological instrumentation for the wind

data collection. According to AWEA, an accuracy range between (0.1 - 0.2 m/s) is

acceptable by the industry if the meteorological instruments used are properly calibrated.

For the purpose of this research work, and according to GEO, the meteorological

instruments used on site were properly calibrated, the data accuracy between (0.1 -

0.2m/s) was assumed for the wind speed.

2.3.3 Structure of Wind Measuring System

According to wind industry practice, the following methodology could be used for wind data acquisition, processing and measurement to determine a high level of accuracy in wind speed data:

• The anemometer - used for signal conditioning and sampling, and converts wind

speed into a physical signal, usually an electrical pulse signal.

• The signal conditioning converts the signal from the anemometer into an

appropriate signal for sampling.

• During pre-processing, the average, maximum and standard deviation of the wind

speed were calculated.

• During data collection all recorded data were stored in a memory with sufficient

resolution of at least 10 bits.

• Data processing, which can then take place using a personal computer and

software to process and give the results of the measurements.

The system parts which contribute to the most critical system accuracy are the calibration

of the anemometer and the method of signal conditioning and sampling.

Typically, the calibration of an anemometer takes place in a wind tunnel, where there is an interaction between the anemometer and wind tunnel. However, care must be

taken to avoid improper placement of the anemometer as this can easily cause errors of

about 0.5 m/s or more. On the other hand, proper scientific calibration procedure, in

which the interaction between wind tunnel and the anemometer is properly performed,

can result in a good calibration accuracy of up to 0.1 m/s.

Hence, the best total system accuracy that can be realized with "low-cost"

equipment is 0.1m/s. This can be achieved by the use of an excellent scientific

anemometer calibration and the right method of signal conditioning and sampling.

2.3.4 Wind Power Cost and Financing

According to a study by researchers at the Lawrence Berkeley Laboratory’s

Energy and Environment Division, Wiser and Kahn (1996), wind energy costs can be cut

substantially if a wind project is owned by a utility, and could also be significantly

reduced if wind developers could obtain the same financing terms as gas power plant

developers.

In fact, Wiser and Kahn (1996) of Lawrence Berkeley Laboratory estimated that a

typical 50-MW wind turbine farm, which could deliver power at just under 5 cents/kWh

when financed by a wind developer, could generate at 3.5 cents/kWh, nearly a 30%

reduction if financed and owned by an investor-owned utility (IOU).

The proposition by Wiser and Kahn, which was examined by members of the

wind industry, concludes that wind projects would be cheaper if they could take

advantage of the lower-cost financing available to other large electric utilities. The duo

examined six ownership and financing scenarios in addition to a wind plant owned by a private developer selling power to a utility under a power purchase agreement (PPA) and to a plant owned by an IOU. They also looked at four scenarios involving public utility ownership, and the conclusion of the study was that if the installation and general O&M costs are the same in all ownership scenarios, the estimated cost of energy for utility ownership of wind power facilities will be significantly lower than the estimated cost of energy owned by private ownership, project-finance structure and IOU ownership by approximately 30% (1.4 cents/kWh).

Internally-financed public utility ownership is estimated to reduce overall costs by approximately 10-40%, depending on whether Renewable Energy Production Incentive

(REPI) costs are included in the analysis. The REPI is a payment to public utilities to compensate for the fact that since they are not subject to federal taxes, they cannot qualify for the Production Tax Credit (PTC). However, since REPI is not a tax deduction,

money must be appropriated for it each year by Congress, and is thus viewed by the

financial community as subject to considerable risk.

Wiser and Kahn (1996), further stated that utility ownership of wind plants is cheaper due to lower cost debt (interest rate of 7.5% compared to 9.5% for a developer), longer debt payment periods (20 years compared to 12 for a developer), and the absence of a "debt service coverage ratio" (DSCR) requirement. The DSCR is a mechanism by which a lender reduces risk of default on a loan by requiring that a wind project generate enough cash each year to exceed loan payments. Typically, this results in a smaller loan than would be most advantageous for the developer.

The study, Wiser and Kahn (1996), makes comparison of wind farm to gas-fired power plants and they both share the opinion that, the seeming risks related to wind turbine technology and the availability of wind resources, a privately owned wind project developer would generally receive financing that is more costly with heavy weighted restrictions than financing available to a more traditional energy generation sources by gas-fired plants. If wind developers received similar financing terms and costs as gas- fired NUGs (non-utility generators), the nominal cost of wind power might further decrease by 25% (1.2 cents/kWh).

The relatively low return on equity (12%) that is required by investors in gas projects compared to 18% for wind projects is the most important variable in wind power costing and financing, according to Wiser and Kahn, If a similar return is required for wind farm projects, the cost would drop from 4.95 cents/kWh to 4.05 cents/kWh, a reduction of 18%.

Wiser and Kahn noted that policy options that could help overcome some of the disparity between gas and wind power generation exist, and some of these policies could be the establishment of a long-term agreement and a possible stable and predictable wind market in U.S. These policies would ultimately help reduce the finance costs by decreasing the market risks associated with wind power development.

The study reported a more direct approach and the involvement of government subsidy, such as low interest rate loans, loan guarantee programs, interest-rate buy-downs that could also be implemented at the state or federal levels to help promote investment and reduce financing costs.

In conclusion, Wiser and Kahn estimated that wind power costs are dependent on ownership and financing method, as shown in Table 2.2:

Type of Ownership Type of Financing Cost of Wind Power

Private ownership Project financing 4.95 cents/kWh including PTC, 6.56 cents/kWh without PTC. IOU ownership Corporate financing 3.53 cents/kWh including PTC, 5.9 cents/kWh without PTC. Public utility ownership Internal financing 2.88 cents/kWh including REPI, 4.35 cents/kWh without REPI. Public utility ownership Project financing 3.43 cents/kWh including REPI, 4.89 cents/kWh without REPI. Table 2.2: Estimate of Wind Power Cost, Source: http://www.awea.org/faq/cost.html (Use with permission for educational purpose)

2.3.5 Wind Turbine Energy Production Estimate

As the energy production of a wind turbine is proportional to the cube of the long-

term average wind speed (v³), a small deviation in the accuracy of wind speed will highly

affect the calculation of energy production. The relative deviation of the calculated

energy production is calculated using the formula;

Figure 2.15, illustrates error in the calculation of annual energy production. For

example, if the yearly average wind speed is 6 m/s, and is measured with a system

accuracy of +/- 0.4 m/s, the error in the calculation of the annual energy production is

approximately +/- 20%.

Figure 2.15: Illustration of Error in Calculation of the Annual Energy Production, Source: www.ekopower.nl (Use with permission for educational purpose)

2.3.6 The Accuracy of the Period for Return on Investment

Financing is a critical issue for a successful wind farm development. According to

AWEA, the cost of electricity from utility-scale wind system has dropped by more than

80% over the past 20 years. In the early 1980s when the first utility-scale wind turbines were installed, wind-generated electricity cost as much as 30 cents/kWh. Presently, the state-of-the-art wind power plants at excellent sites are generating electricity at less than

5 cents/kWh. Costs are continuing to decline as more efficient and larger wind turbines

are built and advanced technology is introduced.

The period for Return on Investment (ROI) can be calculated by;

ROI (years) = INVESTMENT / PROFIT (yearly average)

The profit is proportional to the energy production and therefore, the relative

deviation of the calculated ROI is equal to the relative deviation of the energy production.

Table: 2.3, illustrates the significant influence of the accuracy of wind speed data to profit

and ROI from wind energy production.

The accuracy of ROI is determined for different cases, however, the values for the

calculated ROI are assumed values, and could be different. The Operation and

Maintenance, (O&M) costs were ignored in this analysis and the difference between the

low and high ROI strongly depends on the accuracy of wind speed.

Total Accuracy Yearly Average Relative Deviation When Accuracy of Wind Speed Wind Speed (m/s) of Annual Energy ROI is ROI (Years) (m/s) Production (+/- %) (Years)

0.5 6 25 12 9 – 15

0.2 6 10 12 10.8 - 13.2

0.1 6 5 12 11.4 - 12.6

0.5 8 18.7 8 6.5 - 9.5

0.2 8 7.5 8 7.4 - 8.6

0.1 8 3.7 8 7.7 - 8.3

0.5 10 15 6 4.1 - 5.9

0.2 10 6 6 5.6 - 6.4

0.1 10 3 6 5.8 - 6.2

Table 2.3: Influence of the Accuracy of Wind Speed Data on ROI Source: www.ekopower.nl (Use with permission for educational purpose)

To obtain high period accuracy for (ROI), accurate wind speed data with total

system accuracy of 0.1 to 0.2 m/s is required. For research in areas with moderate average wind speed, it is important to measure the wind speed as accurately as possible

since the economic feasibility of wind energy systems may be critical in such a case.

The extra costs for accurate wind monitoring are relatively very small compared

to a high investment in a wind energy project. This way, more security about the

investment can be purchased inexpensively by using an accurate wind monitoring system.

The period for ROI, i.e. accuracy of ROI (years) can save a lot of money as it can

prevent wrong decisions for investments in wind energy projects. Therefore, in order to 40

obtain the high accuracy of the wind speed data, the anemometer calibration must be

carried out with proper scientific calibration procedure.

2.4 Wind Speed Predictive Models

As soon as a potential site has been located, a more detailed and a relatively expensive investigation are required in order to confirm the feasibility of the project. The wind farm energy output and the financial viability of the project will be very sensitive to the wind speed effect on the turbines over the life of the project. It is therefore, not generally considered acceptable in a complex terrain to rely on the estimates of wind speed made during the initial site selection but to use the measure–correlate–predict

(MCP) technique to establish a prediction of the long-term wind resources (Derrick,

1993; Mortimer, 1994).

The MCP approach is based on taking a series of measurements of wind speed at the wind farm site and correlating them with simultaneous wind speed measurements made at a local air port or meteorological station. The averaging period of the site- measured data is chosen to be the same as that of the meteorological station data.

The MCP requires the installation of a mast at the wind farm site on which the anemometers (usually cup anemometers) and a wind vane are mounted. Typically, one anemometer is mounted at the hub height of the proposed wind turbines with other anemometer install at a lower height to allow wind shear to be measured. Measurements are made over a period of at least one year, even though the more data obtained the more confidence there will be in the result of analysis correlated with the measurements made with data from the meteorological station.

In a simple implementation, linear regression is used to establish a relationship

between the wind speed at the measured site and long-term wind data collected from a meteorological station. There have been various MCP methods and algorithms that have been studied using wind data from a number of potential wind farm sites. Some of the algorithms and methods have been improved using probabilistic approach and implemented into software programs.

An example of this is the software called WindPRO, developed by the Danish

Energy Consultant, EMD International A/S using wind flow modeling inputs from WAsP or CFD software, which is wind energy software used to model wind farms. With this software, users are able to design wind farms, including wind turbine layout and electrical designs. Energy production, turbine noise levels, turbine wake losses and turbine suitability can also be calculated using this software.

The MCP module for WindPRO includes several features that would enable completion of a full MCP analysis within a short period of time, which includes the following:

1. Long-term data - these are measured long-term meteorological wind speed data

2. Measurement - these are load of time series data with filtering

3. Correlation - these are the extraction of concurrent data with correlation analysis

4. Prediction - using Linear Regression, Matrix method, Weibull Scale and Wind

Index

5. Generation of wind statistics directly from the MCP result

Further research study suggests that MCP methods model the relationship

between wind data (speed and direction) measured at the target site usually over a period

of a year, as well as concurrent data at a nearby reference site (Rogers et al. 2005).

The model is then used with long term data from the reference site, e.g., (a local airport

and meteorological station) to predict long-term wind speed and direction distributions at

the target site. These results provide long-term mean wind speed and wind speed distributions at the target site.

Using estimates for regional wind resources, the electrical power producing potential of wind energy can be estimated, and to distinguish between the different types of wind energy potential that can be estimated, the World Energy Council (1993)

identified the following five categories:

1. Meteorological potential; these are equivalent to available wind resource.

2. Site potential; this is based on meteorological potential, but is restricted to sites

that are geographically available for power production.

3. Technical potential; this is calculated from the site potential and accounts for

available technology.

4. Economic potential; this is the technical potential that can be realized

economically.

5. Implementation potential; this takes into account constraints and incentives to

assess the wind turbine capacity that can be implemented within a certain period.

In order to obtain the most useful wind power development, any uncertainties in the predictions need to be understood and clarified.

Conventional MCP techniques assume that the wind direction distribution at the

site is the same as that of the meteorological station. Recent investigations suggest that

this assumption is a source for significant error and a correlation technique based on artificial neural networks was suggested according to the study (Addison et al. 2000).

During this study, the researcher did not perform an in-depth research in artificial

neural network, but rather concentrated on the conventional MCP techniques. Therefore,

for the purpose of this research work, conventional MCP techniques such as a simple

regression analysis, time series analysis and scatter-plot analysis were used as the method

of data analysis.

To perform further research beyond the statistical analysis in wind power

generation modeling, the concept of fuzzy logic was evaluated by the researcher and

incorporated into this research work.

However, the concept of fuzzy logic is not used in this research study to make comparison with statistical analysis, but rather used as a different modeling tool to complement the statistical analysis in the selection of wind farm location, which is a critical aspect to a successful wind farm project implementation.

In doing so, the researcher developed two different fuzzy logic models, namely, the angular model and triangular model using the C# (C-Sharp) programming language to assess wind turbine performance and selection of wind turbine location.

2.5 Fuzzy Logic Studies

The fuzzy set theory was introduced by Zadeh in 1965 and can be seen as an infinite-valued logic. Prior to 1965, Zadeh's work has been concentrated on system theory

and decision analysis. Thereafter, his research interests have shifted to the theory of fuzzy

sets and its applications to artificial intelligence, linguistics, logic, decision analysis,

control theory, expert systems and neural networks.

Zadeh (1965) study on “fuzzy sets” introduced the concept of a class with unsharp boundaries and marked the beginning of a new direction by providing a basis for a qualitative approach to the analysis of complex systems in which linguistic rather than

numerical variables are used to describe system behavior and performance.

The basic principles of Zadeh (1965) theory are: (1) In fuzzy logic, exact

reasoning is viewed as a limiting case of approximate reasoning; (2) In fuzzy logic,

everything is a matter of degree; (3) Any logical system can be fuzzified; (4) In fuzzy

logic, knowledge is interpreted as a collection of elastic or, equivalently fuzzy constraint

on a collection of variables; and (5) Inference is viewed as a process of propagation of

elastic constraints, Aziz (1996).

The basis of the theory lies in making the membership function lie over a range of

real numbers from (0.0 to 1.0). The real world is vague and assigning rigid values to

linguistic variables means that some of the meaning and the value is invariably lost. The fuzzy set theory attempts to follow more closely the vagueness that is inherent in most natural language and in decision-making processes.

In a conventional logic approach, this inherent fuzziness of membership and

categorization is not incorporated. Fuzzy logic has found many real-world applications that involve imitating or modeling human behavior for decision-making in the real world.

In this research work, the researcher has incorporated the use of two different

fuzzy logic concept models to analyze the wind turbine performance based on the

variability of wind speed at different location. These are:

1. The Angular Model, and

2. The Triangular Model

These models will be discussed further in more detail in chapters 4 and 5.

CHAPTER 3

METHODOLOGY OF STUDIES WIND DATA ACQUISITION & ANALYSIS

3.1 Introduction

The purpose of this study is to determine the prospect of a wind turbine

installation using data obtained from a wind speed study and other meteorological study

conducted at a proposed site in Toledo, Ohio. This site location was selected to study the

potential of wind turbine to generate electrical power supply for the area in the city of

Toledo, Ohio. The researcher used the following two different methodologies to perform

a qualitative analysis on the wind speed data obtained.

1. Statistical Models

2. Fuzzy Logic Models

3.2 Statistical Analysis Studies

A statistical analysis and fuzzy logic models were used to perform wind speed

resource analysis to determine if it is feasible to construct a wind farm for electrical

energy generation in the vicinity of the proposed site in Toledo, Ohio.

During a recent wind study conducted in Toledo by Green Energy Ohio (GEO

2009), wind monitoring instruments were installed on November 14, 2007, through

March 24, 2009 on the 300 ft Lucas County Emergency Management System (EMS)

communication tower, to collect wind data. This proposed site is located in Toledo, Ohio.

Measurements were collected at approximately 43m, 61m and 79 m above the

ground surface. Turbulence sources at the site include heavily developed residential areas to the north, east and south, and a few commercial buildings to the west of the site (See

Figure 3.1).

Lucas County EMS Tower Location

Figure 3.1: Aerial View of the Toledo Site, Source: Green Energy Ohio (Use with permission for educational purposes)

The Lucas County EMS tower was selected for this study because of its proximity

to the Toledo Site (i.e., approximately 4,500 feet west-southwest of an area being

evaluated in the vicinity of a proposed wind turbine).

This communication tower is a 300 ft (91.5 m) triangular lattice tower, which is supported by a series of tension cables. The wind study team installed six booms at nominal heights of approximately 43 m, 61 m and 79 m with two booms at each height in the 10° (north-northeast) and 190° (south-southwest) azimuth orientation.

Each boom was fitted with one NRG Maximum Type 40 Anemometer to record the wind speed and the wind direction was measured at each height on the 190° oriented boom using NRG 200P wind vanes. Finally, a temperature sensor was installed at a height of 30 ft (9 m) on the tower to measure atmospheric temperature.

The wind data were recorded at 2-second intervals and then averaged to 10- minute periods and stored using the Symphonie Data Logger. In addition to the wind speed, wind direction and temperature, data were collected and stored on the data logger, which also calculated and stored the standard deviation, maximum and minimum values for each 10-minute period for each sensor.

According to GEO Report (2009), the study officially commenced on November

14, 2007 and ended on February 27, 2009 and during the monitoring period, the data recovery from all the sensors was 93.7%. Some of the data losses were caused from icing events (2.4%), a stolen data card (3.2%), GEO staff error (0.5%), loss of continuity of one of the cables (0.2%), and the omission of data encountered during the passage of

Hurricane Ike (0.05%). The Incident Log for specific details on missing data is attached in Appendix- A.

As soon as data were downloaded each week, they were reviewed by GEO staff using Microsoft Excel macros to ensure the sensors were still functional. At the end of each month, visual inspections of the graphs were conducted to flag spurious data.

Events related to icing, sensor malfunctions, low wind values and turbulence were

recorded and processed using Excel macros. A monthly summary was created after the

spurious data had been flagged and filtered.

A summary of the 16 month average wind statistics for the site at each height tested is

shown in Table 3.1.

Summary of Statistics - Toledo Site

Description 43m 61m 79m (140 ft) (200 ft) (260 ft) Average Wind Speed (m/s) 4.7 5.3 5.7

Cubic Average Wind Speed (m/s) 5.5 6.1 6.3

Prevailing Wind Direction WSW W WSW

Turbulent Intensity (Standard Dev / m/s) 0.22 0.19 0.17

Wind Power Density (W/m2) 97.5 132.8 160.9

43m to 61m 61m to 79m Wind Shear Exponent 0.3668 0.3441

Table 3.1: Summary of Statistics -Toledo Site

GEO Report (2009) indicates that wind speed data were collected at 2-second intervals and then averaged automatically at 10-minute intervals and stored in raw form in the data logger. The data were collected each week and monitored for sensor or hardware failures. At the end of each month, GEO personnel collected and stored the raw data generate for each height.

3.2.1 Wind Direction

To achieve effective wind turbine performance, it is essential to assess the wind direction of any potential site development for siting and positioning a wind turbine. An assessment of the wind direction was conducted to determine the effect of any potential interference (blockage) to the wind direction and/or if a wind turbine installed at a particular position could cause a potential wake that would disturb the wind direction of another wind turbine.

During the study, a wind speed, wind direction and atmospheric temperature data were collected at the site at heights of approximately 43m, 61m and 79m, which corresponds to instrumental labeling Channel 1 & 7, Channel 4 & 8 and Channel 6 & 9, respectively. Also, during the study, predominant wind directions were recorded as follows;

Channel 1 & 7 - (WEST-SOUTH-WEST),

Channel 4 & 8 - (WEST),

Channel 6 & 9 - (WEST-SOUTH-WEST)

The directional wind data were collected and analyzed using software made by NRG-

Symphonie Data Retriever Software to generate a Wind Rose plot.

Figures 3.2, 3.3 and 3.4 are the Wind Rose plots for the three different heights generated using the NRG- Symphonie Data Retriever software.

N 12/1/2007 to 1/31/2009 .17 Wind Rose Ch 1, 7 .15 .22 SITE 6192 Toledo

.15 .19 Site Information: Project: Location: Toledo Zoo .15 .15 Elevation: 634 ft Anemometer on channel 1: NRG 40 S/N 21174 Height: 260 ft W .15 .13 E Serial #: 21174 Vane on channel 7: NRG 200 P S/N 309 Height: 260 ft .15 .11 Serial #: 309 Outer Numbers are Average TIs for speeds greater than 10 mph .14 .11 Inner Circle = 0% Outer Circle = 30%

.14 .11 Percent of Total Wind Energy .13 S Percent of Total Time

Generated Tuesday, November 10, 2009 Total 10-minute intervals: 61632 Intervals used in calculations: 57114 Percent data used: 92.7 NRG Systems SDR Version 6.00

Figure 3.2: A Wind Rose Plot of Wind Direction for Toledo Site at 79m

N 12/1/2007 to 1/31/2009 .18 Wind Rose Ch 4, 8 .18 .18 SITE 6192 Toledo

.17 .19 Site Information: Project: Location: Toledo Zoo .16 .17 Elevation: 634 ft Anemometer on channel 4: NRG 40 S/N 105 Height: 200 ft W .17 .15 E Serial #: 105 Vane on channel 8: NRG 200 P S/N 302 Height: 200 ft .17 .14 Serial #: 302 Outer Numbers are Average TIs for speeds greater than 10 mph .17 .13 Inner Circle = 0% Outer Circle = 20%

.19 .16 Percent of Total Wind Energy .23 S Percent of Total Time

Generated Tuesday, November 10, 2009 Total 10-minute intervals: 61632 Intervals used in calculations: 57114 Percent data used: 92.7 NRG Systems SDR Version 6.00

Figure 3.3: A Wind Rose Plot of Wind Direction for Toledo Site at 61m

N 12/1/2007 to 1/31/2009 .26 Wind Rose Ch 6, 9 .21 .30 SITE 6192 Toledo

.19 .26 Site Information: Project: Location: Toledo Zoo .19 .21 Elevation: 634 ft Anemometer on channel 6: NRG 40 S/N 110 Height: 140 ft W .19 .19 E Serial #: 110 Vane on channel 9: NRG 200 P S/N 307 Height: 140 .21 .18 Serial #: 307 Outer Numbers are Average TIs for speeds greater than 10 mph .22 .16 Inner Circle = 0% Outer Circle = 30%

.19 .16 Percent of Total Wind Energy .18 S Percent of Total Time

Generated Tuesday, November 10, 2009 Total 10-minute intervals: 61632 Intervals used in calculations: 57114 Percent data used: 92.7 NRG Systems SDR Version 6.00

Figure 3.4: A Wind Rose Plot of Wind Direction for Toledo Site at 43m

A histogram of wind speed frequency distribution for the study period was

prepared using the wind speed data were collected from the anemometers to provide a

better measurement for the distribution of the wind speed and an estimated time period

the proposed site is expected to be within a certain range of wind speed during the year.

Additionally, this illustration provides an indication of an estimated time a wind turbine

is expected to be operational for energy generation at the proposed site.

Typically, most large utility-scale wind turbines have “cut-in” wind speeds of between 3 to 4 m/s (i.e. 7 to 9 mph). These turbines would therefore be operational only during the time periods when the wind speed is greater than the minimum “cut-in” threshold speed for the wind turbine.

Moreover, the data distribution illustrated on the histogram provides information on the highest sustained wind speeds that can be expected, which is the highest wind speed the turbine would need to withstand during operation. If the wind speed exceeds the turbine’s “cut-out” speed, the turbine would typically rotate away from the direction of the wind and adjust its blade to avoid excessive wind loading on the blade, or stop spinning and completely “shut off” to avoid potential damage to the turbine during periods of high wind.

An illustration of a frequency distribution histogram of the monthly wind speed averages at different heights; 43 m, 61 m and 79 m are shown in Figures 3.5, 3.6 and 3.7, respectively. Also, a graphical illustration of the monthly average wind speed for the study period is shown in Figure 3.8.

Toledo Site - 79 m Frequency Distribution 20%

15%

10%

0% <1 3 4 5 6 7 8 1 2 9 10 - 11 - 12 13 - 14 - 15 - 16 - 17 - 18 - 19 - 20 - 21 - 22 23 ------5 6 7 8 9 4 2 3 10 - - 11 12 14 15 16 17 18 19 20 21 22 24 13 23

Wind Speed (m/s)

Figure 3.5: A Frequency Histogram Distribution for Monthly Average Wind Speed at 79m Height – Anemometer

Toledo Site - 61 m Frequency Distribution 25%

20%

15%

10%

0% <1 1 2 3 4 5 6 7 8 9 10 - 11 - 12 - 13 14 - 15 - 16 - 17 - 18 - 19 - 20 21 - 22 - 23 ------2 3 4 5 6 7 9 8 10 - - 11 12 13 15 16 17 18 19 20 22 23 24 14 21

Wind Speed (m/s)

Figure 3.6: A Frequency Histogram Distribution for Monthly Average Wind Speed at 61m Height – Anemometer

Toledo Site - 43 m Frequency Distribution 25%

20%

15%

10%

0% <1 3 4 5 6 7 8 1 2 9 10 11 - 12 - 13 - 14 - 15 - 16 - 17 18 - 19 - 20 - 21 - 22 - 23 ------5 6 7 8 9 4 2 3 10 - - 12 13 14 15 16 17 19 20 21 22 23 24 11 18

Wind Speed (m/s)

Figure 3.7: A Frequency Histogram Distribution for Monthly Average Wind Speed at 43m Height - Anemometer

Toledo Site - Monthly Average Wind speed

16 Variable 79m 15 61m 43m 14 )

h 13 p m ( 12 d e e

p 11 S

d n i 10 W 9

7 Month Nov Jan Mar May Jul Sep Nov Jan Year 2007 2008 2009

Figure 3.8: Illustration of Monthly Average Wind Speed at; 43m, 61m and 79m.

3.2.2 Cubic Wind Speed

Since a wind turbine’s power output depends not on the wind speed itself but on the cube of the wind speed, the cube of the wind speed is calculated and analyzed based on the raw speed data collected. From the data collected for this research work, the researcher calculated the cubic wind speed using the formula below, and the graphical illustration is shown in Figure 3.9;

The cubic wind speed average is a more representative number to characterize the site, and it was calculated using the formula in equation (1):

(1)

Source: GEO Report - 2009

where;

Vi = the average wind speed for a 10-minute period

n = the number of such periods in the month

Thus, the reason cubic wind speed average is not the same as the simple wind speed average is that higher wind speeds are more heavily weighted, which accounts for

the turbine’s nonlinear power curve. If the wind blew at constant velocity at all times, the

simple average and the cubic average would be the same. At a site with variability in

winds, the distinction can be important.

A graph for the average monthly cubic wind speed at different heights; 43m, 61m

and 79m, respectively is illustrated in Figure 3.9.

Toledo Site - Monthly Average Cubic Wind Speed

18 Variable 79m 17 61m 43m 16 ) h p

m 15 (

e 14 e p S 13 d n i

W 12

c i b

u 11 C 10

Month Nov Jan Mar May Jul Sep Nov Jan Year 2007 2008 2009

Figure 3.9: Illustration of Monthly Average Cubic Wind Speed at Different Heights; 43m, 61m and 79m.

3.2.3 Wind Shear Analysis

A consideration of wind shear is important when proposing a site for a wind

turbine installation. Wind shear is defined as the change in horizontal wind speed with a

change in height. Since wind speed varies with location and normally, wind increases as

elevation above the ground surface increases. Therefore, it is important to determine the

accuracy of a wind shear exponent from the wind data collected at different heights for a specific site location.

The wind shear exponent is helpful in estimating the power density and potential turbine power output, as such, determining the optimal height of the tower in wind

60 turbine, as well as to estimate the variable load on a turbine’s blades is very essential. If inaccurate wind shear value is used, it would underestimate the power density and potential turbine power output.

The wind shear exponent (α) was determined at the site, because its magnitude is influenced by site-specific characteristics.

The following equation (2) is used to model the increase in wind velocity with increase in height.

α z = (2) z

₀ ₀ where; 𝑉𝑉 𝑉𝑉 � �

V = the wind velocity,

V₀ = the wind velocity with subscript o indicates a reference height

Z = the height

Z₀ = the height with subscript o indicates a reference height

And the coefficient (wind shear exponent), α is determined as;

α = 𝑉𝑉₂ (3)

� 𝑉𝑉₁ � 𝑍𝑍₂ 𝐿𝐿𝐿𝐿𝐿𝐿₁₀ � 𝑍𝑍₁ � where;

V2 = the wind speed at height Z2; and

V1 = the wind speed at height Z1

According to Wind Resource Handbook (1997), the wind shear exponent (α) is approximately 0.143 for a clear, topographical level open land.

The equation (3) was used to calculate the wind shear exponents at the site. The calculated average wind shear was approximately 0.37 for heights between 43m and 61m

(43m to 61m) and 0.34 for heights between 61m and 79m (61m to 79m). These calculated values were higher than a typical clear, topographical flat level land due to obstructions caused by buildings and trees in the vicinity of the site area.

In wind shear modeling, wind shear values that extrapolate from low-level speed data without proper account for the exact calculated high wind shear would underestimate the power density and potential turbine power output at the actual design height of the proposed turbine. The calculated wind shear value for the study period is illustrated in

Figure 3.10.

Toledo Site - Monthly Average Wind Shear

Variable 43m to 61m 0.45 61m to 79m ) e (

n 0.40 e n o p x E

a 0.35 e h S

d n i

W 0.30

0.25 Month Nov Jan Mar May Jul Sep Nov Jan Year 2007 2008 2009

Figure 3.10: Illustration of Monthly Average Wind Shear at Different Heights; (43m to 61m) and (61m to 79m).

These high values of wind shear exponents may be attributed to several factors, such as obstructions caused by buildings and trees in the vicinity of the study area for the proposed site and the general topography of the area.

As a reference to the data collected, recorded wind shear exponents measured at different site monitoring locations in the past were collected to make a comparison with the measured data for the proposed site location, and differences were observed in wind shear data recorded at other different locations compared to the recorded wind shear data at the proposed site is illustrated in Table 3.2.

NASA Plum Bowling Green Toledo Zoo Port Clinton Location Brook, (2008) (2000) (2008) (May 08 – Apr ’09) Height 30m 40m 30m 40m 43m to 61m 61m 38m 73m to to to to to to to 40m 50m 40m 50m 79m 73m 100m Wind Shear Exponent 0.46 0.51 0.32 0.65 0.37 0.34 0.31 0.39 (α )

Table 3.2: Average Wind Shear Data Measurement Recorded at Other Site Locations

3.2.4 Wind Turbulent Intensity

Turbulence in wind is usually caused by the dissipation of wind's kinetic energy

into thermal energy through the creation and destruction of progressively smaller gusts.

Turbulent wind may have a relatively constant mean over time periods of approximately

an hour or more, but over shorter times as minutes or less it may be quite variable.

Turbulent Intensity (TI) is a measure of how turbulent (unsteady) the wind is for each period. This TI is defined as the standard deviation of the wind speed divided by the average (mean) wind speed for the chosen averaging period (TI = Std Dev / Mean).

= (4) 𝜎𝜎 𝑇𝑇𝑇𝑇 � µ �

where; = the standard deviation of wind speed, and µ = the mean wind speed

𝜎𝜎

A data logger of the type shown in photo in Figure 3.11 (equipped to calculate the standard deviation) was installed at the proposed site for wind speed measurement and

64 data collection, and also programmed to perform measurement and calculate the

“standard deviation” over a 10-minute average wind speed as well as the average wind speed for each anemometer installed.

Figure 3.11: Photo of NRG Symphonie-Data Logger; Courtesy of NRG Software. (Use with permission for educational purposes)

As illustrated in Figure 3.12, the recorded turbulent intensities on the data logger ranged from 15% to 23%, with high variation usually during the summer months due to thermal heat and lower average wind speed. Also, some of the factors that may account for this high intensity values are due to the effect of heavily developed residential areas to the north, east and south, and a few commercial buildings to the west of the monitoring site.

From the analysis of the data collected and the graphical illustration in Figure

3.12, the turbulent intensities is lower at a higher elevation since there is less effect from

surrounding buildings and trees, indicating that taller wind turbines installation would be

preferred at this location.

Toledo Site - Monthly Average Turbulent Intensity

0.24 Variable 79m 0.23 61m 43m 0.22 ) % (

0.21 y t i

s 0.20 n e t

n 0.19 I

t n

e 0.18 l u b

r 0.17 u T 0.16

0.15

Month Nov Jan Mar May Jul Sep Nov Jan Year 2007 2008 2009

Figure 3.12: Illustration of Monthly Average Turbulent Intensity at Different Heights; 43m, 61m and 79m.

The phenomenon of wind turbulent intensities is such that the turbulent intensity values decrease as the wind speed increases. Due to high turbulent intensity recorded at the proposed site, a scatter-plot analysis on the turbulent intensity data and the wind speed data was prepared to evaluate the relationship between wind speed and turbulent

intensities and the effect of wind speed on turbulent intensities at a higher elevation. This

relationship is illustrated in Figure 3.13.

Toledo Site - Turbulent Intensity, (TI) vs Wind Speed, (WS)

0.24 Variable 79m TI * 79m_WS 0.23 61m TI * 61m_WS 43m TI * 43m_WS

) 0.22

T 0.21 (

, y t i 0.20 s n e

t 0.19 n I

t 0.18 n e l u

b 0.17 r u T 0.16

0.15

7 8 9 10 11 12 13 14 15 16 Wind Speed,(WS) - (mph)

Figure 3.13: Illustration (Scatter-plot) of Turbulent Intensities and Average Wind Speed at Different Heights; 43m, 61m and 79m.

3.2.5 Air Density

The normal value of air density is specified as 1.225 kg/m3, as the standard

conditions at sea level defined as 15 ⁰C and 1013.3 mbar which, for dry air, correspond to

a density of 1.225 kg/m3. For a given wind velocity, the energy in wind depends on the

air density; therefore, to be able to correct for any change in air density, the air

temperature and pressure would need to be measured.

In this study, the air density is calculated as a function of the ambient temperature, the elevation of the site and the channel height of the specific anemometer. The following equation (5) was used in this calculation;

+ ( 0.034 ( ) 273 + . ρ = [353.05 / (273 + temp.)] * 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑐𝑐ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 ℎ𝑒𝑒𝑒𝑒𝑒𝑒 ℎ𝑡𝑡 (5) − ∗ 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 Source: Wind Resource Handbook, 𝑒𝑒pg 9-8, 1997

where;

ρ = is the density in kg/m³

temperature = the site temperature, (⁰C)

elevation = the site elevation

channel height = the height of anemometer above ground (m)

3.2.6 Wind Power Density

The average wind power density was calculated to determine the Wind Class for the proposed site location. This wind power density class (see Table 3.3) is based on a rating established by the National Renewable Energy Laboratory (NREL), and the power density is calculated using the general density equation (6) as follows;

1 power density = ρ 3 (6) 2

where; ρ = the air density, and V𝑉𝑉 = the wind velocity

Toledo Site Average Wind Power Density (W/m2)

79 m 61 m 43 m

169.1 133.2 98.1

Table 3.3: Average Wind Power Density for Toledo Site

For the purpose of mapping the geographical variation of the wind resource, wind power density was chosen in preference to wind speed because the power density value combines the effect of the distribution of wind speeds and the dependence of the power density on air density than on wind speed.

The wind data were evaluated for average wind power density, which is calculated and described as illustrated in Figure 3.15;

Toledo Site - Monthly Average Wind Power Density

300 Variable 79m 61m

43m ) 250 2 m / W

( 200

y t i s n

e 150 D

r e w

o 100 P

d n i

W 50

0 Month Nov Jan Mar May Jul Sep Nov Jan Year 2007 2008 2009

Figure 3.15: Illustration of Monthly Average Wind Power Density, (W/m2) at Different Heights; 43m, 61m and 79m.

The researcher also obtained a wind speed data from Port Clinton, Ohio during the period of June 2008 through February 2009, where similar wind monitoring activities were performed. This data was recorded at height of 73m, and was used to compare with the measured data at the Toledo Site at height 79m, with both locations relatively close in height.

The result of the analysis show the measurement at Port Clinton yielded higher wind speed and power density than the Toledo Site as shown in Figures 3.16 and 3.17.

The difference in measurement was attributed to the surface roughness surrounding each site. As discussed earlier, the Toledo Site is located at an urban area with significant surface roughness in the form of residential and commercial structures with trees.

In contrast, the Port Clinton Site is located in the vicinity close to Lake Erie, which is an open area with fewer building structures and trees.

Comparison of Monthly Average Wind Speed

8.5 Variable Toledo Site (79m) 8.0 Port Clinton (73m)

7.5 ) s / 7.0 m (

e 6.5 e p S

d 6.0 n i W 5.5

5.0

4.5 Month Nov Jan Mar May Jul Sep Nov Jan Year 2007 2008 2009

Figure 3.16: Comparison of Monthly Average Wind Speed at (Toledo, Ohio and Port Clinton, Ohio).

Comparison of Monthly Average Wind Power Density

Variable Toledo Site (79m) 500 Port Clinton (73m) ) 2 m /

W 400 (

y t i s n

e 300 D

r e w o P 200 d n i W

100

Month Nov Jan Mar May Jul Sep Nov Jan Year 2007 2008 2009

Figure 3.17: Comparison of Monthly Average Wind Power Density, (W/m2) at (Toledo, Ohio and Port Clinton, Ohio).

3.2.7 Regression Fit Analysis and Trend Analysis

The researcher collected a 10-year historical data for the weather station at Toledo

Express Airport. This data was obtained from the National Climatic Data Center (NCDC) which maintains historical weather data sets from various weather stations across the state of Ohio and the nation.

This data was intended for use as the reference station data to correlate with the monitored site (Toledo Site). Among other weather station in the area, the researcher selected the referenced weather station at Toledo Express Airport due to similarities in terrain as far as flat land and proximity to the monitored site (Toledo Site).

Figure 3.18 depicts the location of the reference station, the Toledo Express

Airport with reference to the Toledo Site (monitored station) and the distance between the two locations is approximately 20.9 miles.

20.9 miles

Figure 3.18: The Toledo Site Distance from the Reference NCDC Historic Data Station (Toledo Express Airport) Source: Prepared Using Google Maps

The historical data obtained from NCDC for the average wind speed was used to

correlate the average wind speed for the reference site to the monitored site to determine

if there was any significant trend of record during the period the data were recorded.

Some of the trends examined were either climatology conditions around the reference

station or changes in environmental conditions such as erection of new building

structures or demolition, tree growth or brush clearing.

From the data collected, a probability plot analysis was prepared using Minitab

Statistical Software to determine if there was any significant difference in the wind speed

data recorded. The graphical display of the result in Figure 3.19, shows a slight increase in the annual average wind speed at the monitored site compared to the historic data from

NCDC that was used as the referenced site.

Probability Plot of Toledo Site vs NCDC Historic Data Normal - 95% CI

99 Variable Toledo Proposed Site 95 NCDC Historic Data

90 Mean StDev N AD P 10.34 1.441 33 0.424 0.301 80 8.988 1.201 33 0.948 0.014 70 t

n 60 e

c 50 r

e 40 P 30 20

1 5.0 7.5 10.0 12.5 15.0 Data

Figure 3.19: Comparison of Wind Speed Probability Plot for Toledo Site and NCDC Historic Data.

The researcher performed a time-series analysis to compare the data obtained

from NCDC to the monitored site (Toledo Site). The result of the analysis shows the

average historical wind speed data from the NCDC and data from the monitored site

appears to have a similar pattern of data distribution; however, the historical data from

NCDC appears to be slightly lower than the monitored site as shown in Figure 3.20.

Time Series Analysis for 'Toledo Proposed Site' & 'NCDC Historic Data'

13 Variable Toledo Proposed Site NCDC Historic Data 12 ) h p m (

11 d e e p

S 10

d n i w 9 e g a r e

v 8 A

Month Nov Jan Mar May Jul Sep Nov Jan Year 2007 2008 2009

Figure 3.20: Comparison of Time Series Analysis of Average Wind Speed for Toledo Site and NCDC Historic Data.

Furthermore, the historic data from NCDC was used to perform a scatter-plot with

regression as shown in Figure 3.21. A regression fitted line plot analysis was used to

measure the percentage of variation in the dependent variable (Toledo Site) that is

explained by the regression line as shown in Figure 3.22.

The result of the regression analysis provides the r-square (R2) value, a value also known as the coefficient of determination which is defined as the fraction of total squared error in the model. The (R2) has a value from 0 to 1, with a higher value indicating a good

fit. The R2 value obtained from the regression analysis is 0.812, which is considered a

high value, and is one indication that there is a good correlation fit between the monitored site and the historic data reference site.

Earlier statistical studies have shown that an R2 value greater than or equal to 0.70

(≥ 0.70) is an indicative of a sufficient fit for a strong positive linear regression using the

reference site historical data to correlate the monitored site data. Based on the R2 value of

0.812, the researcher was confident that the historical data obtained from NCDC could be

used as the reference site for regression analysis and prediction of wind speed at the

monitored site.

Scatterplot of Toledo Proposed Site vs NCDC Historic Data

12 e t i S

11 d e s o p

o 10 r P

o d e

l 9 o T

7 7 8 9 10 11 12 NCDC Historic Data

Figure 3.21: A Scatter plot Analysis of Average Wind Speed for Toledo Site and NCDC Historic Data.

Regression Fitted Line Plot for Toledo Proposed Site vs NCDC Historic Data Toledo Proposed Site = 0.602 + 1.083 NCDC Historic Data

15 Regression 95% CI 14 95% PI

13 S 0.666056 e R-Sq 81.2% t i

S R-Sq(adj) 79.9% 12 d e s

o 11 p o r 10 P

d 9 e l o T 8

6 7 8 9 10 11 12 NCDC Historic Data

Figure 3.22: A Regression Analysis of Average Wind Speed for Toledo Site and NCDC Historic Data.

Residual Plots for Toledo Proposed Site Normal Probability Plot Versus Fits 99 2

90 l t

a 1 n u e d c 50 i r s e e P R 0 10

1 -1 -2 -1 0 1 2 8 10 12 Residual Fitted Value

Histogram Versus Order

6.0 2 y

4.5 l c

a 1 n u e d i u s

q 3.0 e e r R 0 F 1.5

0.0 -1 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Residual Observation Order

Figure 3.23: A Summary of Residual Plot Analysis of Average Wind Speed for Toledo Site and NCDC Historic Data.

3.2.8 Wind Power Generation Analysis

In an effort to determine how much power can be produced from the wind at the proposed site, several wind power curves were obtained from the manufacturer. The turbine power curves obtained were valid for a particular air density value, which depends on the air temperature and pressure.

Typically, wind turbine manufacturers perform turbine ratings at various air densities and other manufacturers provide ratings based on standard air density of 1.225 kg/m3. During the monitoring period at the Toledo Site, the average air density was 1.21

kg/m3. Since the average air density at the monitored site is relatively close to the standard air density (1.225 kg/m3), the standard air density was used for the power

generation analysis as listed below in Table 3.5.

The power output for each turbine model could be determined over a 12-month

period for the year 2008. A summary table of the projected energy output results and

percent capacity for the two different makes of turbine is listed in Table 3.4.

The percent capacity is defined as the ratio of projected energy output to the

energy output if the turbine was operating at full capacity. Due to environmental and

mechanical factors such as icing and secondary electrical loss, a wind turbine may not

always operate at the above calculated values, hence the net energy and capacity

projections were included.

Also included is a summary of gross and net energy output and gross and net

capacity as calculated by Windustry-Wind Power Calculator.

Toledo Site Turbine Characteristic and Energy Output

Manufacturer Capacity Dia. Hub Projected Gross % Projected Net Percent of Height Gross 12- Percent Net 12- Capacity Rotor Month Capacity Month Energy Energy Output Output (kWh) (kWh) Type-A 1500kW 77m 80m 2,729,980 20.4 2,473,028 18.0 Type-B 1800kW 80m 80m 2,495,113 15.7 2,264,735 13.2

Table: 3.4 Summary of Wind Turbine Energy Output and Capacity Factor for 12 months

It can however be concluded that the calculated data from the “Type-A” turbine

theoretically outperform the larger “Type-B” turbine based on the result in Table: 3.4.

This conclusion suggests that “Type-A” turbines are more efficient at lower wind speed

thus, resulting in a larger energy output even though its rated energy output capacity is

lower.

This analysis also explains smaller wind turbines, which are designed for low

speed operation can actually perform better depending on the site location where the

turbine is installed. The energy output calculations are based on the measured wind

speeds and an air density of 1.225 kg/m3. With respect to energy output and percent

capacity, newer models of low speed turbines have been developed, which may improve in capacity ratings better than the results as reported in the summary Table 3.5.

Figures: 3.26 and 3.27 is a graphical illustration of the 12 months energy output that could be expected for the Toledo Site.

Toledo Site 12-months Energy Output from Measured Wind-Speed for Type A -Wind Turbine

250,000

200,000

150,000

100,000

Energy (kWh)Output Energy 50,000

0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 Wind speed (mph)

Figure 3.24 Type A-Turbine, 12 Months Projected Energy Output (kWh) for Wind Speed at 79m

Toledo Site 12-months Energy Output from Measured Wind-Speed for Type B-Wind Turbine 250,000

200,000

150,000

100,000

Energy (kWh)Output Energy 50,000

0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50

Wind speed (mph)

Figure 3.25: Type B-Turbine, 12 Months Projected Energy Output (kWh) for Wind Speed at 79m

Wind farm power curve is a model that allows the prediction of wind farm power

for a predicted wind speed and direction. This modeling can be carried out using different

methods. In this study, the turbine power curves used to calculate the power produced

were obtained from the manufacturers, which was used to plot and provide a graphical

illustration of power production at different speeds.

The manufacturer’s models assumed a turbulent intensity of 10 – 15% and air

density of 1.225 kg/m3. Figures 3.29, 3.30 and 3.31 illustrate the power curve for the

wind turbine Type-A, Type-B and Type-C, respectively. The Type-A models have a cut- in speed of 7.8mph and cut-out speed of 49.2 mph and the Type-B models have a cut-in speed of 8.9mph and cut-out speed of 55.9mph, while the Type-C models has a cut-in speed of 8.9mph and cut-out speed of 44.7mph.

The Type-B turbine has an advantage over the other two turbines (Type-A and

Type-C) because it has a longer cut-out speed, which means it can withstand higher wind speed than the other two turbines. The Type-B turbines are currently used in Bowling

Green, Ohio for wind power electricity generation.

Power Curve - Type-A Turbine 1600 1400 1200 1000 800 600 400 200 0 Electrical Power Output (kW) Output Power Electrical 0 10 20 30 40 50 60 Windspeed (mph)

Figure 3.26: A graphical display of Type-A wind turbine power curve Source: http://www.inl.gov/wind/software/ (Use with permission for educational purpose)

Manufacturer: Type-A Model Conversion Factor Turbine: 77m rotor 1 m/s = 2.23693mph

Rated: 1500kW Cut-In Speed=7.8 mph

Cut-Out Speed=49.2 mph

Power Curve - Type-B Turbine 2000 1800 1600 1400 1200 1000 800 600 400

Electric Output (kW) Power Electric 200 0 0 10 20 30 40 50 60 Windspeed (mph)

Figure 3.27: A graphical display of Type-B wind turbine power curve Source: http://www.inl.gov/wind/software/ (Use with permission for educational purpose)

Manufacturer: Type-B Conversion Factor Turbine: 80m rotor 1 m/s = 2.23693 mph

Rated Power: 1800kW Cut-In Speed=8.9 mph

Cut-Out Speed=55.9 mph

1600 Power Curve - Type-C Turbine

1400

1200

1000

800

600

400 Electricla Power Output (kW) Output Power Electricla 200

0 0 10 20 30 40 50 60 Windspeed (mph)

Figure 3.28: A graphical display of Type-C wind turbine power curve, Source: http://www.inl.gov/wind/software/ (Use with permission for educational purpose)

Manufacturer: Type-C Conversion Factor Turbine: 77m rotor 1 m/s = 2.23693mph

Rated Power: 1500kW Cut-In Speed=8.9 mph

Cut-Out Speed=44.7 mph

According to AWEA (2008) report, new manufacturing companies were seen entering the U.S. market in wind turbine installations. The report shows that GE Energy continues to dominate the market with 43% of the newly installed capacity in 2008 and over 48% of the over 5,000 turbines installed in 2008. Vestas Co. and Siemens Co. retained the second and third place respectively in capacity installed in 2008. 85

The Figure 3.28 illustrates turbine manufacturer’s installation (MW Capacity) in 2008 by percentage (%).

Turbine Manufacturers Percentage (MW Capacity) Install in 2008 REpower Acciona WP AWE 1.2% Fuhrlander DeWind 4.8% 0.0% 0.1% 0.0% Category GE Energy Mitsubishi other Vestas 6.0% 0.0% Siemens Suzlon Clipper Gamesa 7.0% Clipper Mitsubishi Acciona WP GE Energy Gamesa 42.7% REpower 7.2% Fuhrlander DeWind AWE other Suzlon 8.6%

Siemens 9.2% Vestas 13.1%

Figure 3.29: Turbine Manufacturer’s Installation (MW Capacity) in 2008 by (%); Source: AWEA Report 2008, (Use with permission for educational purposes)

CHAPTER 4

ASSESSMENT OF WIND TURBINE SYSTEM USING THE ANGULAR MODEL

4.1 Introduction

As discussed in Chapter 3, a statistical analysis was performed on the wind data obtained during the current research to determine the performance of a wind turbine system for a proposed wind turbine installation at the Toledo site. A further analysis and evaluation of wind turbine performance is discussed in this chapter using the concept of fuzzy logic. During the study, the researcher developed an angular fuzzy logic model using the C# (C-Sharp) programming language to evaluate the performance of a wind turbine system.

The angular fuzzy logic model was first introduced by Hadipriono and Sun

(1990). As applied in the current study, this model uses the fuzzy set angular approach to interpret the fuzzy linguistic values which were obtained by subjective assessment of the wind turbine performance by the user. The model uses an angle to determine a line that represents a linguistic value, whereby the membership value is a function of angle (α).

This fuzzy set angular model can be applied to fuzzy logic “modus ponens” and

“modus tollens” deduction techniques. Additionally, this fuzzy logic operation describes

the truth functional modification (TFM) and the inverse truth functional modification

(ITFM), which can be performed by simple calculation procedures.

The linguistic expressions and the corresponding values of each expression is tabulated and illustrated in Table 4.1 and Figure 4.1 respectively.

Linguistic Expression Angular Value (θ)

Absolutely Positive 90° Very Positive 67.5° Positive 45° Fairly Positive 22.5° Undecided 0° Fairly Negative -22.5° Negative -45° Very Negative -67.5° Absolutely Negative -90°

Table 4.1: Linguistic Expressions and their corresponding Angular Values

The use of angular model in this research work is to complement the statistical analysis performed in Chapter 3. The researcher created a corresponding linguistic expression for the angular model, (Table 4.2), using the National Renewable Energy

Laboratory (NREL) Wind Power Classification table, (Table 4.3).

As illustrated in Table 4.3, for example, if the average wind speed of a proposed wind farm location is 26.6 mph in the 50m height category, the wind power class will be classified as > 8, which has a corresponding linguistic expression of “Absolutely

Positive” in Table 4.2. Similarly, when the average wind speed of a proposed wind farm location is 17.9 mph in the 50m height category, the wind power class will be classified as 6, which has a corresponding linguistic expression of “Fairly Positive” as illustrated in

Table 4.2.

A more detail examples on how the angular model can be used to determine wind turbine performance using Tables 4.2 and 4.3 are illustrated in Figures 4.5, 4.6 and 4.7 of the Wind Turbine Illustration Model Examples.

The user (wind farm designer/ developer) can determine the performance of a wind turbine based on the average wind speed data available for a proposed wind farm location using the angular model.

Figure 4.1: Angular Fuzzy Set Model

4.2 Approximate Reasoning

Zadeh (1975) stated that approximate reasoning is viewed as a process of

approximate solution of a system of relational assignment equations. It is obvious that much of human reasoning is approximate rather than precise in nature.

For example as human beings, we reason in approximate terms when we decide on how to cross a traffic intersection. Perhaps the simplest way to characterize fuzzy logic is to say that it is logic of approximate reasoning, a process that can describe the

“fuzzy modus ponens” and “fuzzy modus tollens” deduction techniques.

The modus ponens relationship can be simply stated as: [S ∧ (S → P)] → P

The following is a “modus ponens” propositions:

Antecedent 1: (s is S) ⊃ (p is P) S, S’ ⊂ U

Antecedent 2: (s is S’) P, P’ ⊂ V

______

Consequent: Q (p is P) is τB and (p is P) τP ⊂ T (1)

Where the symbol ⊃ is a representation of the implication relation between (s is

S) and (p is P); s and p are the names of objects; S and S’ are fuzzy sets in the universe of discourse U, while P and P’ are fuzzy sets in universe of discourse V; Q (p is P) means

“the truth of (p is P)’; τP is the new truth fuzzy set value in truth space T and the symbol

“ ⊂ ” denotes ‘ a subset of ’.

When there is a fact that shows S’ = S, the result can then be obtained. However,

when the value of S’ does not match exactly with S, then the fuzzy inference is in order.

This form of inference can be considered as a fuzzy modus ponens deduction (FMPD)

which is reduce to the classical modus when S’ = S and P’ = P. The objective of FMPD is

to find the value of P’.

The “fuzzy modus tollens” relationship can be simply stated as:

[(S →P) ∧ ∼ P] → ∼ S

The following is a “modus tollens” propositions:

Antecedent 1: (s is S) ⊃ (p is P) S, S’ ⊂ U

Antecedent 2: (p is P’) P, P’ ⊂ V

______

Consequent: Q (s is S) is τS and (s is S’) τS ⊂ T (2)

Where τS is the new truth fuzzy set value in the truth space T, and this method of inference is called the fuzzy modus tollens deduction (FMTD) which is reduced to

“modus tollens” when P’ is not P and S’ is not S. This can be used to find the truth value of a certain proposition that has an implication for relation with a second proposition when information for the second proposition is available.

To facilitate the solution of FMPD, it is necessary to use fuzzy logic operations such as the Truth Functional Modification (TFM) and Inverse Truth Functional

Modification (ITFM).

4.3 Angular Fuzzy Set Model [Excerpted from Hadipriono and Sun (1990)]

4.3.1 TFM Using Angular Models

TFM is a logic operation that can be used to modify the membership function of a linguistic value in a certain proposition with a known truth value. This principle adopted from Baldwin (1978) illustrates a problem that is transformed into the truth space, which can be described as follows (Hadipriono and Sun (1990):

Ω: (s is S) is τS: S ⊂ U: τS ⊂ T (3)

where, the truth restriction τS is the value of T. The modification of this proposition yields

the following:

Ω: (s is S’) is τS: S ⊂ U: (4)

As a result, the membership function can now be solved as follows:

ФS’ (z) = ФτS [ФS (z)] (5)

where ФS’ (z) and ФS (z) are the membership functions of proposition S’ and S,

respectively, and ФτS (t) is the membership function of truth restriction τS.

Applying the angular model to equation (5) will represent the membership function values of ФS’ (z) as values of the angles.

Suppose that the angles of A and τS in the angular fuzzy set model (AFSM) are α

and β respectively, as shown in Figure 4.2, the membership functions, ФS (z) and

ФτS (t) can be written as:

ФS (z) = z tan α and ФτS (t) = t tan β (6)

Figure 4.2: Angular Model

Source: Hadipriono and Sun (1990)

The combination of equations (5) and (6) provide the following equation below:

ФS’ (z) = ФτS = [z tanα] = z tan α tan β

such that; ФS’ (z) = z tan γ (7)

where; tan γ = tan α tan β (8)

Using equation (8), we can obtain the modified membership function of a linguistic value by a simple mathematical calculation procedure. For example, suppose Ω is the proposition ‘The performance of the wind turbine is High, (HI) is Fairly True, (FT)’, and we wish to calculate the performance of the wind turbine. Therefore, using AFSM, the linguistic value HI and FT can be characterized by α = π/4 and β = π/8 respectively.

From the statement above, equation (8) yields:

tan γ = tan (π/4) tan (π/8) = tan (π/8)

Assuming that the same characteristics of linguistic values are used in each space

(U, V and T), the angle tan γ = (π/8) in space U can be interpreted as “Fairly High (FH)”.

Hence, the TFM of the proposition provides: ‘The wind turbine performance is Fairly

High’.

4.3.2 ITFM Using Angular Model

The ITFM, developed by Baldwin (1978) is a logic operation that can be used to

obtain the truth values of a conditional proposition. Suppose we have a proposition:

Ω: (s is S), but it is known from given data that (s is S’). We can calculate the truth of

proposition Ω as follows:

Q (s is S/s is S’) = τS; τS ⊂ T; S, S’ ⊂ T (9)

where τS is the new truth restriction of the truth value for fuzzy set S. The membership

function of the new truth value, ФτA (t), can be obtained from the following:

ФτS (t) = ФτS [ФτS (z)] = ∨ [ФτS (z)] (10) z

where ∨ denotes the value where z is maximum. The proposition Ω now z becomes:

Ω’: (s is S) is τS. Therefore, the proposition described above can be written as:

Ω: (s is S)/ (s is S’); S, S’ ⊂ U

Ω: (s is S) is τS: τS ⊂ T (11)

Suppose that the membership functions of S and S’ are characterized by angles α and α’

as shown in Figure 4.3

Figure 4.3: Angular Model

Source: Hadipriono and Sun (1990)

The membership functions ФS (z) = ФS’ (z) can be written as:

ФS (z) = z tanα; and ФS’ (z) = z tanα’ (12)

Substituting equation (12) into equation (11), the membership function, ФτS (t),

characterized by the angle β, now yields:

ФS (z) = ФτS [ФS (z)] = ФτS [z tanα] = z tan α tan β and ФτS (t) = ∨ [ФS’ (z)] = ∨ [z tan α’] z z

so that; z tan α tan β = z tan α’. For z ≠ 0, we would have;

tan β = tan α’/tan α (13)

and, ФτS(t) = t tan β = t tan α’/ tan α (14)

Equation (14) gives a very simple mathematical equation for the Inverse Truth

Function Modification. For example, suppose Ω is the proposition “this performance of the wind turbine is fairly high (FH),” and we wish to assess the truth of Ω if we have the knowledge that this wind turbine performance is high (HI) is true (TR)’. The linguistic value FH and HI can be characterize by α = 1 π/8 and α’= π/4, respectively.

Through equation (13), we have:

tan β = tan (π/4)/ tan (1π/8) = 2.414214 and β = 3π/8

Hence, the truth value is Very True and the original proposition Ω can be written linguistically as:

‘Q (this wind turbine performance is fairly high given that it is high) is Very True’.

4.3.3 Fuzzy Modus Ponens Deduction

Consider again, the proposition in equation (1). Through the ITFM,

‘Antecedent 2: (s is S’)’ in equation (1) can be combined to become;

Ω: (s is S) is τS ⊃ (p is P) (15)

The truth value of (p is P), i.e., τP, can be obtained by modus ponens deduction.

Mathematically, the membership function of the truth value τP, is given by;

ФτP (y) = ∨ [ФτP (x) ∧ Ф1 (x, y)] (16) x

where; x and y are the elements (truth levels) of the truth spaces T and T’ of the propositions (s is S) and (p is P) respectively; Ф1 (x, y) is the truth implication relation function of the proposition (s is S) ⊃ (p is P) and the symbol ∧ denotes minimum or

conjunction of ФτS (x) and Ф1 (x, y).

For the AFSM, we need to define a new truth implication relation function Ф1 (x, y).

Using the Lukasiewicz law, the implication relationship function of proposition S and P

by traditional models, and according to Giles (1976), is:

< S ⊃ P> = sup {0,

– }

~~where, ,~~

~~and ~~are the membership functions of propositions S, P and S ⊃~~~~

~~P respectively. 99~~

~~From Lukasiewicz theory, the membership function of implication relation, using~~

~~Angular Fuzzy Set Model, is given as:~~

~~Ф1 (x, y) = y – x (x ≥ 0, y ≥ 0) (17)~~

~~Substituting this implication relation function into equation (16), the membership function of the truth value τP can be obtained as follows:~~

~~Following Blockley (1980) approach, a shorthand form of writing is adopted; for~~

~~example, if we use S instead of (s is S), and P instead of (p is P), equation (1) can be~~

~~written in a more general manner as:~~

~~[S ⊃ P] is (p is P) is τ1: S, S’ ⊂ U~~

~~S’ is τ2: P, P’ ⊂ V (18)~~

~~______~~

~~Q (P) is τP and P’: τP ⊂ T~~

~~where, τ1 and τ2 are the truth restriction of implication relation Ф1 (x, y) and of~~

~~proposition (s is S’), respectively.~~

~~In order to simplify the description, we use z tan S, z tan S’ and t tan τP to~~

~~represent the membership function ФS (z), ФS’ (z) and ФτP (t), respectively. Since a~~

~~proposition is usually expressed by its membership functions, such as;~~

~~S = ФS (z) = z tan S, S’ = ФS’ (z) = z tan S’, etc. Using the TFM and ITFM, the~~

~~proposition S and S’ can be combined as:~~

~~Q (S/s’) = τS = ITFM [S/TFM (S’, τ2)]~~

~~100~~

~~= ITFM{S/[S’ tan τ2]} (19)~~

~~i.e. τA = t tan S’ tan τ2/tan S (20)~~

~~in which TFM (S’, τ2) ,the shorthand form of S’ is modified by truth value τ2’~~

~~and ITFM (S/S’) is the shorthand form of Inverse Truth Functional Modification of a~~

~~proposition S given S’. Now the proposition in equation (18),can then be rewritten as:~~

~~[S is τS ⊃ P] is τ1: S ⊂ U: P, P’ ⊂ V~~

~~______(21)~~

~~Q(P) is τP and P’: τP ⊂ T~~

~~and τP can be calculated by equation (16). This can be rewritten as:~~

~~ФτP (y) = ∨ {ФτS (x) ∧ TFM [Ф1 (x, y), τ1]} (22) z~~

~~substituting equation (17) into equation (22), we have:~~

~~ФτS (x) = x tan τS = TFM [(y – x), τ1]~~

~~= (y – x) tan τ1~~

~~and x = y tan τ1 /[tan τ1 + tan τS] (23)~~

~~so that we have,~~

~~ФτP (y) = y tan τP = ФτS (x) = x tan τS~~

~~= y tan τ1 tan τS /[tan τ1 + tan τS]~~

~~and, tan τP = tan τ1 tan τS /[tan τ1 + tan τS] (24)~~

~~101~~

~~From equation (24) and knowing the truth of Q(P) is τP , the proposition P can be~~

~~modified by TFM (P,τP) to give the value of P’. That is:~~

~~ФP’ (z) = TFM (P, τP) = ФP (z)tan ,τP = z tan B tan τP (25)~~

~~Combining equations (20), (24) and (25), we have:~~

~~ФP’ (z) = [z tan τ1] [tan S’] [tan τ2 ][tan P] / [tan S tan τ1 + tan S’ tan τ2] (26)~~

~~Equation (26) can be used to calculate the membership function of proposition (p is P’).~~

~~As an example of a modus ponens deduction, consider the following propositions:~~

~~“IF the wind speed is Very High (VH), THEN the turbine performance is GOOD (GD), that is true (TR).” If it is True (TR) that wind speed is Fairly High (FH), what is the conclusion?~~

~~This can be written as:~~

~~[wind speed is VH ⊃ performance is GD] is TR~~

~~[wind speed is FH] is TR~~

~~______~~

~~Q (turbine performance is GD) = τP and performance is P’~~

~~Suppose that based on the AFSM, we have;~~

~~tan (GD) = tan (TR) = tan (π/4), tan (VS) = tan (3π/8), and tan (FS) = tan (π/8)~~

~~Through the use of equations (20), (24) and (25), we have:~~

~~102~~

~~tan τP = tan (TR) tan (VS) tan (TR) [tan (S) tan (TR) + tan (VS) tan (TR)]~~

~~= 0.707107~~

~~and, tan (P’) = tan (GD) tanτS = 0.707107 tan (π/4)~~

~~= 0.707107~~

~~So that P’ is between Fairly Good, α = π/8 and undecided, α = 0~~

~~If we replace:~~

~~‘[wind speed is VH ⊃ performance is GD] is TR’ by~~

~~‘[wind speed is VH ⊃ performance is GD] is Absolutely True (AT)’~~

~~then we have tan (AT) = tan (π/2) and~~

~~tan τP = Limit tan(AT) tan (VS) tan (TR) AT→π/2 [tan (FS) tan (AT) + tan (VS) tan (TR)]~~

~~= tan (π/8) = 5.82843~~

~~thus; tan P’ = tan (GD) tan τP = 5.82843 tan (π/4)~~

~~= 5.82843~~

~~103~~

~~4.4 Illustration of the Angular Model~~

~~To demonstrate the wind turbine performance system using the angular model, the~~

researcher developed a graphical user interface (GUI) program using the C# (C-sharp) software programming language to illustrate how the variability of wind speed can be used to assess the wind turbine performance.

~~The program uses linguistic variables to describe the turbine performance based on the variability of wind speed. The process of executing the program is illustrated in~~

~~Figure 4.4.~~

~~Following a statistical analysis performed on the wind data collected over a~~

~~minimum period of one year and based on the NREL wind classification table, (Table~~

~~4.3), the user can make a selection of the wind speed and the expected turbine~~

~~performance.~~

The wind power classification values from NREL table can be converted into linguistic expressions and applied to the angular model. These values are represented as linguistic expressions and angular values as tabulated in Tables 4.2 and 4.3, and they can be used in the angular model as follows:

~~104~~

~~Linguistic Expression Angular Value (θ) Wind Power Classification~~

Absolutely Positive 90° > 8 Very Positive 67.5° 8 Positive 45° 7 Fairly Positive 22.5° 6 Undecided 0° 5 Fairly Negative -22.5° 4 Negative -45° 3 Very Negative -67.5° 2 Absolutely Negative -90° < 2 Table 4.2: NREL Wind Power Classification and their Corresponding Linguistic Expressions for the Angular Model

~~10m (33 ft) 50m (164 ft) Wind Wind Power Wind Speed, m/s Wind Power Wind Speed, Power Class Density (W/m2) (mph) Density (W/m2) m/s (mph)~~

~~2 100 4.4 (9.8) 200 5.6 (12.5)~~

~~3 150 5.1 (11.5) 300 6.4 (14.3)~~

~~4 200 5.6 (12.5) 400 7.0 (15.7)~~

~~5 250 6.0 (13.4) 500 7.5 (16.8)~~

~~6 300 6.4 (14.3) 600 8.0 (17.9)~~

~~7 400 7.0 (15.7) 800 8.8 (19.7)~~

~~8 1000 9.4 (21.1) 2000 11.9 (26.6)~~

~~Table 4.3: NREL Wind Power Classification 105~~

~~Figure 4.4: Illustration of Input Screen for Wind Turbine Using Angular Model~~

~~106~~

~~Example 1:~~

From Table 4.3; Let us consider an example of a wind power classification in the 50m category, and assuming that the wind power density is 2000 W/m2 and wind speed is 26.6 mph, the corresponding wind power classification value is class no. 8.

~~Furthermore, from Table 4.2, the linguistic expression that is equivalent to wind power classification No. 8 is “very positive,” which implies that the wind turbine performance is “very good.”~~

~~S: (Wind Speed [WS] is Very High) ⊃ (Performance of Turbine [PT] is Very Good) then: S’ (Wind Speed [WS] is Fairly Slow) ======Therefore: P’ is (between fairly poor and undecided)~~

~~Figure 4.5: Illustration of Wind Turbine Model – Example 1 107~~

~~Example 2:~~

From Table 4.3; Let us consider an example of a wind power classification in the 50m category, and assuming that the wind power density is 600 W/m2 and wind speed is 17.9 mph, the corresponding wind power classification value is class no. 6.

Furthermore, from Table 4.2, the linguistic expression that is equivalent to wind power classification No. 6 is “fairly positive,” which implies that the wind turbine performance is “fairly good,”

~~S: (Wind Speed [WS] is Fairly High) ⊃ (Performance of Turbine [PT] is Good) then: S’ (Wind Speed [WS] is Very High) ======Therefore: P’ is (between good and very good)~~

~~Figure 4.6: Illustration of Wind Turbine Model – Example 2 108~~

~~Example 3:~~

From Table 4.3; Let us consider an example of a wind power classification in the 50m category, and assuming that the wind power density is 400 W/m2 and wind speed is 15.7 mph, the corresponding wind power classification value is class no. 4.

Furthermore, from Table 4.2, the linguistic expression that is equivalent to wind power classification No. 4 is “fairly negative,” which implies that the wind turbine performance is “fairly slow.”

~~S: (Wind Speed [WS] is Fairly Slow) ⊃ (Performance of Turbine [PT] is Poor) then: S’ (Wind Speed [WS] is Slow) ======Therefore: P’ is (between poor and fairly poor)~~

~~Figure 4.7: Illustration of Wind Turbine Model – Example 3 109~~

~~CHAPTER 5~~

~~ASSESSMENT OF WIND TURBINE SYSTEM USING TRIANGULAR MODEL~~

~~5.1 Introduction~~

~~The evaluation of wind turbine performance is generally measured quantitatively~~

~~by the availability of wind resources and the suitability of site location for installation of~~

~~wind turbine. The availability of these resources is directly related to the performance of~~

~~a wind turbine. In this chapter, the triangular model was used to assess the turbine~~

~~performance based on the wind resource and location of the proposed site development.~~

~~With some similarity to other fuzzy models discussed in this research work, a~~

~~user, (design engineers and wind farm developers) makes a subjective selection of~~

~~linguistic variables and assigns ratings of numerical values for the assessment of turbine~~

~~performance. The value of theses linguistic expressions varies from Low (LO), Fairly~~

~~Low (FL), Medium (MD), Fairly High (FH) and High (HI) performance and the~~

~~numerical ratings range from -1.0 to 1.0.~~

~~The fuzzy set value in the model approximates the performance rating to “High,” as the set values increase and shift to the right from 0 to 1.0, while the model~~

~~approximates the performance rating to “Low” as the set values decrease and shift to the~~

~~left from 0 to -1.0. A triangular fuzzy set model is illustrated in Figure 5.1.~~

~~110~~

~~Figure 5.1: Illustration of Triangular Numerical Values~~

~~5.2 The Triangular Fuzzy Set Model~~

~~This model is a graphical representation of the membership values µ(xi) that~~

correlates with the fuzzy components (xi). The linguistic values are illustrated in the shape of triangles that can shift horizontally within the numerical values of -1.0 and 1.0 as shown in Figure 5.1.

~~111~~

~~The fuzzy triangular model for the turbine performance are represented by the~~

~~membership values and fuzzy sets as shown below,~~

~~Low (LO) = [1|-1, 0|-0.5, 0|0, 0|0.5, 0|1]~~

~~Fairly Low (FL) = [0|-1, 1|-0.5, 0|0, 0|0.5, 0|1]~~

~~Medium (MD) = [0|-1, 0|-0.5, 1|0, 0|0.5, 0|1]~~

~~Fairly High (FH) = [0|-1, 0|-0.5, 0|0, 1|0.5, 0|1]~~

~~High (HI) = [0|-1, 0|-0.5, 0|0, 0|0.5, 1|1]~~

~~where the numbers on the left hand side of the delimiter “|” are the membership values~~

~~µ(xi) and the numbers to the right hand side are the fuzzy set values xi. The delimiter “|”~~

~~symbolizes the fuzzy relation between the component and its membership value.~~

~~5.3 Mamdani Inference System Approach~~

~~One of the fuzzy methods that employ the general “IF –THEN” rule structure is~~

~~the Mamdani approach and the following equation applies to this approach;~~

~~Ri: IF xi-1 is A i-1, AND xi-2 is Ai-2 …..THEN y is B1 (for i=1, 2,…k) (1)~~

~~where k is the number of rules, xi is the antecedent variable, and y is the consequent~~

~~variable.~~

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~~The Mamdani fuzzy approach uses a fuzzy controller structure which consists of the following four components;~~

~~1. Fuzzification Unit~~

~~2. Rule Base Unit~~

~~3. Inference Engine~~

~~4. Defuzzification Unit~~

~~Below is the illustration of the fuzzy controller chart:~~

~~Fuzzification Fuzzy Inference De-Fuzzification Process Engine Process~~

~~Fuzzy Rule Base~~

~~Figure 5.2: General Fuzzy Controller Chart~~

~~5.3.1 The Fuzzification Unit~~

~~This unit is where the process of converting the assessment of selected conditions into appropriate fuzzy sets and is called fuzzification. The process converts the input,~~

(antecedent) data from a linguistic expression into quantitative membership through a process of searching membership functions and rules, which then checks to ensure the condition of each rule matches the given input.

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~~In this study, the process transfers the inputs of wind speed and turbine location~~

~~condition to quantitative values ranging from (-0.1 to 1.0), using input membership~~

~~functions.~~

~~5.3.2 The Fuzzy Rule Base Unit~~

~~This unit is where all the rules and conditions are stored. The fuzzy rules are~~

~~collections of linguistic statements that describe how the decision of fuzzy inference is~~

~~made to classify inputs and to control outputs.~~

~~5.3.3 Fuzzy Inference Engine Unit~~

~~This unit is the component that attempts to obtain a conclusion by using the rule~~

~~base. The fuzzified measurements are then used by the inference engine to evaluate~~

~~control rules stored in the fuzzy rule base. The fuzzy rules are a combination of linguistic~~

~~statements of the basic “IF-THEN” statements, which explain how decisions are~~

~~processed in the fuzzy inference engine.~~

~~In other words these are of the format: IF (input 1 is a membership function)~~

~~and/or (input 2 is membership function 2) and/or… then (outputn is output membership~~

~~functionn).~~

~~As illustrated in Table 5.1; For example: If wind speed is “Low” and turbine~~

location is “Low”, then there is a likelihood of “Low” turbine performance. Similarly, if the wind speed is “Fairly High” and the turbine location is “Medium”, then there is likelihood of “Medium” turbine performance.

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~~Wind Speed~~

~~Project Fairly Fairly Overall Low Medium High Condition Low High~~

~~Low Low Low Low Low Low~~

~~Fairly Fairly Fairly Fairly Fairly Low Low Low Low Low Low Fairly Medium Low Medium Medium Medium Low Fairly Fairly Fairly Fairly Low Medium High Low High High~~

~~TurbineLocation Fairly Fairly High Low Medium High Low High~~

~~Table 5.1: Inference Rules with Relationship~~

~~The inference engine rule selects conditions of the “wind speed” and “turbine~~

~~location” and uses fuzzy combination methods to identify the result of a rule. The~~

~~simplest and common fuzzy combination method is the “min-max” method, which uses~~

~~the combination of two operations; the “AND” and “OR” operations.~~

~~These operations are written as follows, respectively;~~

~~UA ∩ B = min (UA (x), UB (x)) (2)~~

~~UC ∪ D = max (UC (x), UD (x)) (3)~~

~~The combination of the two operations is called “max-of-mins” method which can be written as:~~

~~z = [(A1 ∩B1) ∪ (A2 ∩B2) ∪….] (4)~~

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~~The combination of these two operations is represented as illustrated in Figure 5.4;~~

~~Figure 5.4: Max-of-Mins Method for 2 rules involving 2 input and 1 output variables Source: (lsiwww.epfl.ch/.../webcourse/ch09/fuzzy.html) (Use with permission for educational purposes)~~

~~116~~

~~5.3.4 The Defuzzification Unit~~

The conversion of the likelihood of fuzzy set values into a single crisp value is called defuzzification. An example of a defuzzification process is the conversion of the likelihood of poor wind turbine performance to a single crisp unit.

~~The defuzzification processes are classified into seven approaches:~~

~~1. Centroid Method~~

~~2. Weighted Average Method~~

~~3. Mean Max Membership~~

~~4. Max Membership Principle~~

~~5. Center of Sum~~

~~6. Center of Largest Area~~

~~7. First (or last) of Maxima~~

~~5.3.4.1 The Centroid Method~~

This is also called the center of gravity or the center of area, and is one of the most common defuzzification methods because it uses “mathematical integration.” This method can be represented as follows:

~~x# = ∫ µc(x).x dx / ∫ µc(x) dx (5)~~

~~where x# is the defuzzification value. This method is illustrated in Figure 5.5, where the corresponding defuzzification value for the maximum membership is equal to 0.389.~~

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~~Figure 5.5: The Centroid Method~~

~~5.3.4.2 The Weighted Average Method~~

~~This method is the most commonly used defuzzification method because~~

~~of its simple calculation. This method can be represented as follows;~~

~~x# = Σ µc(x#).x / Σ µc(x#) (6)~~

~~where x# is the defuzzification value, and x# is the centroid of the sharing membership~~

~~functions A, B, and C. These values are calculated by measuring the average of the~~

~~centroids of the membership functions A, B, and C, as shown in Figures: 5.6, 5.7 and 5.8.~~

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~~When computed, the overall weighted average gives a total value of the entire shape to be 0.438. This overall membership function value x# is illustrated in~~

~~Figure 5.9;~~

~~Figure 5.6: The Centroid of Membership Function A~~

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~~Figure 5.7: The Centroid of Membership Function B~~

~~120~~

~~Figure 5.8: The Centroid of Membership Function C~~

~~121~~

~~Figure 5.9: The Overall Weighted Average Value~~

~~122~~

~~5.3.4.3 The Mean Max Membership Method~~

~~This method is used in case there are flattened level maximum values instead of a~~

~~single point peak. The mean is calculated using the following equation:~~

~~x# = (a +b) / 2 (7)~~

~~where x# is the defuzzification value. The mean max membership method is illustrated~~

~~with values “a” and “b” as shown in Figures 5.10.~~

~~Figure 5.10: The Mean-Max Defuzzification Method~~

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~~5.3.4.4 The Max Membership Method~~

~~This method is bounded to the peak value of the output function, and it can be~~

~~represented using the following expression:~~

~~µc (x#) ≥ µc(x) for all x ∈ X (8)~~

~~where (x#) is the defuzzification value and the corresponding defuzzification value for the~~

~~maximum membership is equal to 0.3, as illustrated in Figure 5.11.~~

~~Figure 5.11: The Max Membership Defuzzification Method~~

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~~5.3.4.5 The Center of Sums Method~~

~~This method assesses each individual area and calculates the middle point of each~~

~~area. The overall defuzzification value is then determined and calculated by adding these~~

~~sums and then divided by the number of membership functions. The center of sums can~~

~~be calculated using the following equation:~~

~~x# = ∫ x Σµc(x#).x dx / ∫ µc(x#) dx (9)~~

~~where x# is the defuzzification value, and the x# represents the individual graphs. The~~

~~membership functions A and B are illustrated in Figures 5.12 and 5.13, and the total~~

~~overall defuzzification value is 0.35, as illustrated in Figure 5.14.~~

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~~Figure 5.12: The Center of Sums - Membership Function A~~

~~126~~

~~Figure 5.13: The Center of Sums - Membership Function B~~

~~127~~

~~Figure 5.14: The Center of Sums Defuzzification Method - Total Overall Value~~

~~5.3.4.6 The Center of Largest Area Method~~

This method is used where there are two convex subareas; in this example, the defuzzification value is measured using the center of gravity or the centroid method of the largest area. The result of the defuzzification value is 0.4 as illustrated in Figure 5.15.

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~~Figure 5.15: The Center of Largest Area Method~~

~~5.3.4.7 The First (or Last) of Maxima Method~~

This method takes the first value with a maximum of the overall area of membership function. In the last of maxima method, the last value with a maximum of the overall area of membership function is taken. Figure 5.16 illustrates the first and the last maxima method.

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~~Figure 5.16: The First and Last of Maxima Method~~

~~130~~

~~5.4 Illustration of Triangular Model and Mamdani Approach~~

In this research work, the researcher developed a software program using a C# (C- sharp) programming language for the “centroid method” in the Mamdani Approach. The model was developed as graphical user interface (GUI) program to illustrate the evaluation of wind turbine performance. The variables selected for this evaluation were the “wind speed” and “turbine location” as tabulated in Tables: 5.2 and 5.3.

~~To perform the assessment of a wind turbine location, a qualitative analysis using fuzzy logic models are more appropriate than using empirical statistical analysis models.~~

~~As earlier discussed in Chapter 4, the use of fuzzy logic models in this research work is to complement the statistical analysis performed in Chapter 3.~~

To assess the performance of a wind turbine based on the average wind speed and location selection for a proposed wind farm project, the researcher used his knowledge and experience in construction engineering to make subjective choice of the rules base and the conditions used in the inference engine for the development of this software program for the fuzzy logic model using the Mamdani Approach.

~~In addition, the researcher made a subjective selection of five classifications in percentages with an increment of 20% from 0% to 100%. The NREL Wind Power~~

Classification values were then applied to these selections with the corresponding linguistic expressions and applied to the triangular model. These values are represented as linguistic expressions and triangular values as tabulated in Tables 5.2.

Similarly, a subjective selection of percentage range with five classifications in percentages with an increment of 20% from 0% to 100% was made and researcher’s subjective description of different wind turbine locations were assigned the

~~131 corresponding linguistic expressions as tabulated in Table 5.3, and applied to the triangular model.~~

~~Examples of how Tables 5.2 and 5.3 can be used in the triangular model are illustrated in Figures 5.17, 5.18, 5.19 and 5.20:~~

~~Wind Speed (%) Wind Speed Linguistic Expression~~

~~m/s (mph)~~

~~0 – 20 5.6 (12.5) Low~~

~~20 – 40 6.4 (14.3) Fairly Low~~

~~40 – 60 7.0 (15.7) Medium~~

~~60 – 80 7.5 (16.8) Fairly High~~

~~80 – 100 8.0 (17.9) High~~

~~Table 5.2: Use of Wind Speed Linguistic Expressions for Triangular Model~~

~~Turbine Location (%) Location Description Linguistic Expression~~

~~0 – 20 Poor Low~~

~~20 – 40 Fair Fairly Low~~

~~40 – 60 Satisfactory Medium~~

~~60 – 80 Good Fairly High~~

~~80 – 100 Very Good High~~

~~Table 5.3: Use of Turbine Location Linguistic Expressions for Triangular Model~~

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~~The model as illustrated in Figure 5.17, demonstrates the use of the software,~~

~~which has “two tracking” bars located at the top left hand corner of the model for “wind~~

~~speed” and “turbine location.” Also, the model includes 4 sub-graphs that show the~~

~~combination of an AND operation between the two variables.~~

A final graph on the right-hand side of the model display shows the combination of all the 4 sub-graphs and the position of the calculated centroid. The defuzzification method used in the software is the “Centroid Method,” and is calculated using the following equation:

~~x# = ∫ µc(x#).x dx / ∫ µc(x#) dx~~

~~5.4.1 Using the Triangular Model~~

~~A user makes subjective selection of the ratings on the two variables,~~

~~“wind speed” and “turbine location” by sliding the “two tracking bars” created in the~~

~~model. After the selection of these variables, the user clicks on “calculate overall value”~~

~~button and the inference engine processes the AND operation of the two variables and a~~

~~defuzzification process displays a graphical illustration of the values along with a bar that~~

~~shows the position of the centroid on the graph. Also, a numerical value of the calculated~~

~~centroid is displayed at the top right-hand corner of the model.~~

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~~5.4.2 Examples to Demonstrate the Use of the Model~~

~~Example 1:~~

Assuming that a user is considering a design for wind turbine installation at a location with an estimated average wind speed value of 8.0 mph, this value corresponds to a positive value of 10% in the range of 0 – 20%, as illustrated in Table 5.2, and a “fair” turbine location with a positive numerical value of 30%.

~~Output:~~

~~The application of the “centroid defuzzification method” will generate 4- subgraphs and a final graph with a numerical centroid value of 0.07, as illustrated in~~

~~Figure 5.17. This value interprets the turbine performance as “Low”.~~

~~Figure 5.17: The Triangular Model – Using the Mamdani Approach~~

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~~Example 2:~~

Assuming that a user is considering a design for wind turbine installation at a location with an estimated average wind speed value of 13.0 mph, this value corresponds to a positive value of 30% in the range of 20 – 40%, as illustrated in Table 5.2, and a

~~“satisfactory” turbine location with a positive numerical value of 50%.~~

~~Output:~~

~~The application of the “centroid defuzzification method” will generate 4-~~

~~subgraphs and a final graph with a numerical centroid value of 0.30, as illustrated in~~

~~Figure 5.18. This value interprets the turbine performance as “Fairly Low”.~~

~~Figure 5.18: The Triangular Model – Using the Mamdani Approach~~

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~~Example 3:~~

Assuming that a user is considering a design for wind turbine installation at a location with an estimated average wind speed value of 15.2 mph, this value corresponds to a positive value of 50% in the range of 40 – 60%, as illustrated in Table 5.2, and a

~~“satisfactory” turbine location with a positive numerical value of 60%.~~

~~Output:~~

~~The application of the “centroid defuzzification method” will generate 4- subgraphs and a final graph with a numerical centroid value of 0.50, as illustrated in~~

~~Figure 5.19. This value interprets the turbine performance as “Medium”.~~

~~Figure 5.19: The Triangular Model – Using the Mamdani Approach~~

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~~Example 4:~~

Assuming that a user is considering a design for wind turbine installation at a location with an estimated average wind speed value of 17.5 mph, this value corresponds to a positive value of 90% in the range of 80 – 100%, as illustrated in Table 5.2, and a

~~“very good” turbine location with a positive numerical value of 90%.~~

~~Output:~~

~~The application of the “centroid defuzzification method” will generate 4- subgraphs and a final graph with a numerical centroid value of 0.76, as illustrated in~~

~~Figure 5.20. This value interprets the turbine performance as “High”.~~

~~Figure 5.20: The Triangular Model – Using the Mamdani Approach~~

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~~CHAPTER 6~~

~~WIND ENERGY SYSTEMS (ENVIRONMENTAL ASPECTS & IMPACTS)~~

~~6.1 Introduction~~

~~Wind energy is one of the cleanest and most environmentally neutral energy~~

~~sources in the world today. Compared with conventional fossil fuel energy sources, wind~~

~~energy generation does not degrade the quality of our atmospheric air and water, and can~~

~~make important contributions to reducing climate-change effects.~~

~~Wind energy offers several environmental benefits, especially when compared to~~

~~other forms of electricity generation. However, wind energy can also negatively affect~~

~~wildlife habitat and other individual species in the environment, and when this happens,~~

~~adequate measures are required to mitigate the impacts.~~

~~Before developing wind energy projects, permits need to be approved by various~~

~~authorities, such as local permitting authorities, which usually include agencies such as the Local Planning Commission, the Zoning Board, the City Council, or the County~~

~~Board Officials.~~

~~The individual state permitting authorities include agencies such as the~~

~~Department of Natural Resource and Environmental Protection Agencies, State Historic~~

~~Preservation Offices, Industrial Development and Regulation Agencies, Public Utility~~

~~Commissions, or siting boards. While the federal permitting authorities, such as Federal 138~~

~~Land Management Agencies, Bureau of Land Management or the United States Forest~~

~~Service are the managing and permitting authorities.~~

The concerns foremost in many government agencies are whether the wind energy projects pose any risks to the resources and/ or environments they are required to protect. To date, several wind development projects have been given permission and sited across the nation. Since there is a growing market acceptance of wind energy due to the global demand for sustainable energy, the wind energy industry is continuing to address major significant environmental and siting challenges.

~~Some of the concerns made by several environmental activists are related to~~

~~wildlife and birds in particular that are threatened by numerous human activities,~~

~~including effects from climate change. Erickson et al. (2002) noted that anthropogenic~~

~~causes of avian fatalities range from 100 million to 1 billion annually, and some of the~~

~~causes of these fatalities are buildings/windows, house cats, high-tension lines, vehicles,~~

~~pesticides, communication towers and wind turbines.~~

~~In addition, recent National Research Council (NRC 2007) study concluded that~~

~~current wind energy generation is responsible for 0.003% of human-caused avian~~

~~mortality. Currently, it is estimated that for every 10,000 birds killed by all human~~

~~activity, less than one death is caused by wind turbines. Relative to other human causes~~

~~of avian mortality, wind energy’s impacts are quite small.~~

~~Despite the negative impacts of wind energy projects, recent studies suggests that~~

~~further comparative analyses are needed to better understand the trade-offs with other~~

~~energy sources. Avian mortality is also caused, for example, by oil spills, oil platforms~~

~~built on bird migration routes along the Gulf Coast, acid rain, and mountaintop mining.~~

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~~Wind energy will likely continue to be responsible for a comparatively small fraction of~~

~~total avian mortality risks.~~

~~6.2 Visual Impacts of Wind Turbines~~

~~The environmental problems that were associated with avian interaction and wind~~

~~power systems was noticed in the U.S. in the late 1980s, when federally protected golden~~

~~eagles and red-tailed hawks birds were being killed by wind turbines and high voltage transmission lines in wind farms in the state of California.~~

~~There are two primary concerns related to this environmental issue:~~

~~1. Effects on bird population from death caused by wind turbines~~

~~2. Violation of the Migratory Bird Treaty Act and the Endangered Species Act.~~

~~Most commercial turbines produce approximately 1.5 to more than 2 megawatts~~

~~(MW) of power and are typically three-bladed, with a rotor-swept diameter of 70 m or~~

~~more and placed on a tower approximately 80 m tall or more. The majority of these~~

~~modern commercial wind turbines, therefore, have a maximum height measurement from~~

~~ground level to the tip of the blade of approximately 120 m (≈375’) or taller.~~

~~Because of the turbine height, the wind turbine can be highly visible and~~

~~sometimes public reactions to wind turbines are very subjective depending on its~~

~~location. While some people feel that turbines are intrusive, others view them as an~~

~~elegant and interesting tower to view. In fact, according Wind Power in the U.K., a~~

~~Sustainable Development Commission report (May 2005) indicated that some people~~

~~view wind turbines as graceful structures that complement landscapes, particularly when~~

~~compared with the centralized power stations and power lines that have been present~~

~~140 across landscapes for many years. In either case, the visual impacts of wind energy projects would be a factor in assessing the site acceptability by the community.~~

~~Since most commercial-scale wind turbines are installed at a height of at least more than 60 m above the ground, proposed wind projects must be reviewed by the~~

~~Federal Aviation Administration (FAA) for compliance with all the necessary permitting.~~

In February 2007, the FAA updated an advisory circular (FAA, 2007) dealing with obstructions lighting and markings, which includes new uniform recommendations for lighting installation on wind energy projects. The FAA suggestions were designed to allow pilots flying too low to be warned of obstructions and minimize intrusion to neighbors.

The guidance also recommends that wind energy projects should be lit at night, but now the lights can be up to 0.8 km apart and be placed only around the project perimeter, thereby reducing the number of lights needed for the entire project lighting.

~~The guidelines recommend red lights, which are less annoying than white lights to people living nearby and no daytime lighting is necessary if the turbines and blades are painted white or off-white.~~

~~6.3 Avian Interaction with Wind Turbines~~

Most wind farm development projects usually stretch out for many miles and the turbines are installed along with associated infrastructure, such as the access roads to the facility, which can sometime impact animal movements although there has not been any recent quantitative work on the impacts of wind farm developments on animal movements.

~~141~~

~~Winkelman (1990) studied the behavior of birds approaching wind turbines~~

~~during day and night conditions. During the study, specific questions that were raised~~

~~include how many birds pass the turbines at tower height and what proportion of these~~

~~birds that have collided with the turbines. Specialized equipment such as the search~~

~~approach radar, passive image intensifiers in combination with infrared lights and thermal~~

~~image intensifiers was used to determine abundance, behavior and height of birds flying~~

~~at night or during poor visibility. Ninety-two percent (92%) of birds approached the rotor~~

~~without any hesitation during the day compared to forty-three percent 43% during the~~

~~night.~~

~~Erickson et al. (2002) summarized bat fatalities at wind farms, where researchers~~

~~found that some bat mortality can be expected at most wind plants with a very large~~

~~majority of the fatalities involving migratory tree and foliage roosting bats, such as the~~

~~hoary and silver-haired bats in the western United States, and hoary and eastern red bats~~

~~in the midwest and eastern parts of the country.~~

~~Although at one time wind farm workers suggested that lattice towers might~~

~~encourage birds to perch and thus have the tendency to get proximity to rotating blades,~~

~~but recent studies have not confirmed any correlation between tower type and bird~~

~~fatality rates.~~

~~The state of Texas is considered part of the Central Flyway, which runs through the western part of Missouri, Arkansas and Louisiana, and then follows the path of the~~

~~Gulf coast of Mexico. The Central Flyway merges toward the east with the Mississippi~~

~~Flyway and is bounded to the east by the Missouri River. Figure 6.1, illustrates the~~

~~Central Flyway path.~~

~~142~~

~~Figure 6.1: The primary routes taken by migratory birds through Texas and the Lower Gulf Coast. Source: Illustrator: J. Pahountis-Opacic. (Used with permission for educational purpose)~~

~~Sidney A. Gauthreaux, Jr. (Department of Biological Sciences, Clemson~~

University, Clemson, SC) has pioneered the use of weather surveillance radar to monitor bird migrations. The Doppler weather surveillance radar (WSR-88D) on the Gulf coast provides information on the direction and speed of bird movements, which were all displayed in bright colorful images. When properly calibrated, the WSR-88D can be used to measure the density of migrating birds and use the speed of movement relative to the winds to roughly determine the types of birds involved.

~~143~~

Tower lights seem to play a key role in attracting birds, and the lighting of tall structures appears to contribute to avian fatalities as well. In fact, illuminating other taller aerial structures to make them more visible to aircraft has also increased bird fatalities.

~~Studies have shown that tower placement is a site-specific phenomenon but several key conclusions that have been identified were:~~

~~1. Irregularly spaced turbines might increase fatalities because birds try to negotiate~~

~~the apparent gaps between turbines;~~

~~2. Turbines placed close to the edge of ridges show higher fatality rates because~~

~~raptors often hover in such locations, and;~~

~~3. Turbines placed near a gully create higher fatalities among birds because birds~~

~~often use these locations as flight paths.~~

Thus, wind-energy developers are encouraged to work closely with bat and bird ecologists in conducting initial screening of potential development sites. Such screenings are designed to exclude areas of high bat or bird use from being developed as wind energy facilities.

~~6.4 Impacts of Wind Turbine Noise~~

Noise is defined as any unwanted sound. The concern with noise depends on the level of intensity, frequency distribution and patterns of the noise source. The problem associated with wind turbine noise has certainly been one of the more studied environmental impact areas in wind energy engineering.

~~In most cases, the environmental noise are effects of the subjective sound levels that are associated with, and include annoyance, nuisance, dissatisfaction and interference~~

~~144 with activities such as speech, sleep and learning. Figure 6.2, illustrates some of the common noise in the environment.~~

~~Figure 6.2: Decibel levels of Some Common Noise Source: http://www.awea.org/pubs/factsheets/050629_Myths_vs_Facts_Fact_Sheet.pdf (Used with permission for educational purpose)~~

~~145~~

~~The causes of noise emitted from operating wind turbines can be classified into~~

~~two categories, aerodynamic and mechanical noise.~~

~~Aerodynamic noise is produced by the airflow from wind speed over the turbine~~

~~blades and the mechanical noise generated in the nacelle of the turbine is transmitted~~

~~along the structure of the turbine from the gearbox and generator. Generally, concerns~~

~~about sound are primarily associated with older wind turbine technology, such as those~~

~~used in the 1980s, which were considerably louder. The primary sound in wind turbines~~

~~is aerodynamic noise from the blades moving through the air.~~

~~Today, the noise produced by wind turbines has diminished as advances are made in engineering and proper insulation ensures that modern turbines are relatively quiet.~~

~~With improvements in blade airfoils and turbine operating strategy, more of the wind energy is converted into rotational energy, and less into acoustic noise.~~

Noise that is not commonly heard in modern turbines are the mechanical sounds from the generator, yaw drive, and gearbox. Usually, when the wind picks up speed and the wind turbines begin to operate, the sound from a turbine (when standing at or closer to the turbine at more than 350 m) is equal to 35 to 45 decibels (dB). This sound level is equivalent to a running kitchen refrigerator.

Modern turbines can be designed to minimize mechanical noise through special finishing of gear teeth, using low-speed cooling fans and mounting components and parts in the turbine nacelle instead of at ground level. In general, to achieve further reduction in turbine noise, turbine designers need to concentrate on how to further reduce aerodynamic noise.

~~146~~

~~6.5 Land use Impacts of Wind Power System~~

~~Wind farms are sometimes considered to be intrusive rather than land intensive.~~

~~However, since most wind energy system could extend over a large geographic area, the~~

~~physical representation of the actual wind turbine and supporting equipments usually~~

~~covers only a small portion of the land.~~

~~Wind farm development could affect other users on or adjacent to wind farm sites.~~

~~Wind farm projects also tend to be located in areas of low residential density, which further compounds the difficulties of controlling the impact on property value.~~

~~In 2003, the Renewable Energy Policy Project (REPP) conducted a study of~~

24,000 home sales surrounding 11 wind projects in the United States. It compared the average selling price over time of homes near the wind project with a nearby control area that was at least 5 miles from the project. There was no evidence of any adverse effects on property values. In some communities, however, home values near the facilities rose faster than home values in the control group density Stringer, Fredric, and Kostiuk

~~(2003).~~

~~In April 2006, a Bard College study focused on a 20-turbine wind project in~~

~~Madison County, New York. Researchers visited each home and measured the distance to the nearest turbine and determined to what degree the home could see the wind facility.~~

This study also concluded that there was no evidence that the facility affected home values in a measurable way even when concentrating on homes that sold near the facility or those with a prominent view of the turbines, Hoen (2006).

~~147~~

Studies of the effects of wind projects on local property values should be done with great care, even though extensive studies have already been conducted and there were no evidence that wind farm affected home values.

~~148~~

~~CHAPTER 7~~

~~SUMMARY, CONCLUSIONS AND RECOMMENDATION~~

~~7.1 Summary~~

~~The objective of this research study was to simulate the process and procedure for~~

~~planning and assessment of a wind turbine performance for wind power development at a proposed site by a potential wind farm developer.~~

~~The study discussed empirical and qualitative models to analyze several variables~~

~~that can be use to assess wind turbine performance and location of a potential wind farm.~~

~~For this purpose, the use of statistical models and fuzzy logic models were discussed in~~

~~this research work. To accomplish this objective, meteorological instrumentations were~~

~~installed on a tower in the vicinity of the proposed site to measure wind speed at three~~

~~different elevations, wind direction, air density and atmospheric temperature.~~

~~A statistical analysis was performed using statistical models such as time series~~

~~analysis, scatter-plot analysis, regression analysis and trend growth models to evaluate the wind resource for the proposed site. The following statistical summaries, which~~

includes, the average wind speed, cubic average wind speed, turbulent intensity, wind shear exponent and wind power density were obtained from the analysis performed for the proposed site. In addition, the prevailing wind direction was determine from data

~~149~~

~~collected from the wind vane and plotted on a wind rose using a wind rose generating~~

~~software.~~

~~The study also, included two fuzzy logic models developed by the researcher to~~

~~complement the statistical analysis performed in the study such as to subjectively assess~~

~~the performance of a wind turbine based on the wind speed and wind power in relation to~~

~~its location. These fuzzy logic models are; (1) The Angular Fuzzy Logic Model and (2)~~

~~the Triangular Fuzzy Logic Model.~~

~~The angular fuzzy logic applies the fuzzy modus ponens deduction technique with~~

~~respect to the truth functional modification and the inverse truth functional modification.~~

~~The angular model depends on angles, which can be assign a numerical value that would~~

be calculated and the value are then interpreted into linguistic expression. Because of this simple process, the angular model is easy to use and to interpret in terms of linguistic expressions. In addition, the model created in this research allow user to make subjective selection of their linguistic variables and set rules and conditions in assessing the performance of a wind turbine based on the variability of wind speed and wind power classification.

The second model discussed in this research work is the triangular model which shows a relationship in the membership values µ(xi) with the fuzzy components xi. The linguistic values are represented by a fuzzy set relation of triangles that can shift horizontally in the triangular fuzzy model. This model was applied in relation to the

Mamdani approach, which uses the IF-THEN rules. The simplest and common fuzzy combination method is the “min-max” method, which uses the combination of two operations; the “AND” and “OR” operations. The two variables selected in this model

150 were “wind speed” and “turbine location”. This model would assist the users to assess turbine performance based on the selected turbine location and variable wind resource available to them.

In addition, the centroid method of defuzzification was incorporated into the software program to integrate and combine the values and transform the overall result from a fuzzy set into a crisp value that can be interpreted as a linguistic expression.

~~7.2 Conclusion~~

The success of a wind farm for wind power development has been attributed to the availability of wind resource and a suitable turbine location. The conventional empirical approach using statistical analysis to assess the wind farm development was used in this research.

~~This research work describes the result of a wind data analysis performed to evaluate the possibility of a wind power development at a proposed site in Toledo, Ohio.~~

Based on the analysis performed on the data collected, a summary of statistics on wind data analyzed is reported in this study. Due to the high recorded values of wind shear and turbulent intensity, industrial buildings located west of the monitored site and several residential buildings located north, east and south of where the monitored instrumentation was located for monitoring appeared to have affected wind data collected during the monitored period.

~~From the wind data analysis performed, the annual average wind speed at the monitored site were 4.7 m/s at 43m and 5.3m/s at 61m, with a wind power density of~~

~~133.2 W/m2. Based on the NREL wind speed classification table, this site can be~~

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~~categorized as Wind Power Class 1. The NREL recommends a wind power class 2 to 3~~

~~for a utility scale wind farm. Since the proposed site is not intended for a large scale wind~~

~~farm project, the analysis performed show adequate wind resource for the installation of a~~

~~medium scale utility wind turbine.~~

~~While an empirical approach using statistical models can be employed to analyze~~

~~the non-subjective wind data measured at a proposed site using meteorological~~

~~instrumentation, a qualitative model using fuzzy logic models was used to complement~~

~~the empirical model. To determine a suitable turbine location for wind energy generation,~~

~~a qualitative analysis is required to make a subjective assessment of a wind turbine~~

location. In this study, a qualitative approach using fuzzy logic model was used in making this assessment and since many of the variables used in determining the turbine location use linguistic expressions or qualitative terms, the use of fuzzy logic concept can be employed to transform subjective linguistic expressions into quantitative values and measured.

This research work has demonstrate how specific linguistic terms and their membership values can be used to help assess the overall performance of a wind farm, which can be used to determine the success of a potential wind farm project development.

~~The two software programs development in this research would help the users to easily evaluate a potential wind farm based on the information available to them.~~

~~The purpose of this research work is to develop a system of approach for users to make an assessment of a potential wind farm develop~~

~~ment. The researcher considered the concept of fuzzy logic used in this study as one of the best approaches in making this assessment. The combination of empirical and~~

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~~qualitative approaches in this research study have helped draw a conclusion to the~~

~~significance of using a qualitative approach such as the concept of fuzzy logic to assess~~

~~the performance of a potential wind farm energy development project.~~

~~7.3 Recommendation and Future Study~~

In building a path to America’s Clean Energy future, the government would need to address the inadequacies of the nation’s electric infrastructure policies to help renewable energy development reach their full potential. This research work has helped in developing a process towards achieving that goal. However further expansion and improvement can be made on the study for a better assessment tool for wind farm development. The researcher would like to make the following recommendation for future study;

~~• Future research in the analysis to implement “fuzzy logic power curves” in wind~~

~~farm assessment could be made for comparison to present use of power curve~~

~~prepared by turbine manufacturers. The wind power curve model allows the~~

~~prediction of wind farm power for predicted wind speed and direction. The use of~~

~~“fuzzy logic power curves” can input variables such as wind speed, wind~~

~~direction, air pressure and air temperature and create an output variable (wind~~

~~farm) by means of membership functions and finding the proper transfer functions~~

~~for relating them. The linguistic expressions can then be transfer to create a fuzzy~~

~~logic power curve.~~

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~~• Further improvement in the qualitative approach could be made using different~~

~~types of fuzzy logic models such as the Translational Model and Rotational~~

~~Model to assess a potential wind farm project.~~

~~• Further research in fuzzy logic models could be made to evaluate and compare the~~

~~Translational Model, Rotational Model, Angular Model and Triangular Model to~~

~~determine which model provide a better assessment of a potential wind farm~~

~~project.~~

~~• Further research could be made in collecting more detailed and comprehensive~~

~~wind data for analysis so as to achieve a more accurate wind data for estimating~~

~~wind energy generation and better prediction of wind speed.~~

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~~LIST OF REFERENCES~~

~~[1] Al-Humaidi, H., & Tan, F.H., “Construction Project Delay Analysis Using Fuzzy Set”, The Ohio State University, Columbus, Ohio, 2008~~

~~[2] American Wind Energy Association (AWEA), Annual Wind Industry Report, 2008~~

~~[3] American Wind Energy Association (AWEA), Annual Wind Industry Report, 2007~~

~~[4] Bailey, B.H., & McDonald, S.L., “Wind Resource Assessment Handbook”, National Renewable Energy Laboratory (NREL); AWS Scientific Inc., April 1997~~

~~[5] Baldwin, J.F., “A New Approach to Approximate Reasoning Using a Fuzzy Logic”, University of Bristol, Eng. Mathematics Dept., February 1978, Research Report EM/FS3~~

~~[6] Blanchard, M. & Desrochers, G., “Generation of Auto-Correlated Wind Speeds for Energy Conversion System Studies”, Solar Energy 1984; 33(6):571–9~~

~~[7] Blockley, D.I., “The Nature of Structural Design and Safety”, John Wiley & Sons Ltd, New York, 1980~~

~~[8] Box, George E.P., & Jenkins, G.M., “Time Series Analysis, Forecasting and Control”, San Francisco; Holden-Day, 1976~~

~~[9] Burton, Tony, Sharpe, David, Jenkins, Nick, Bossanyi, Ervin. “Wind Energy Handbook “John Wiley & Sons Ltd, 2001~~

~~[10] Cameron, W.P., Michael, N., “Very Short-Term Wind Forecasting for Tasmanian Power Generation”, IEEE Transactions on Power Systems 2006; 21(No. 2):1–8.~~

~~[11] Chou, K.C., and Cortis, R.B., “Simulation of Hourly Wind Speed and Array Wind Power”, Solar Energy 1981; 26:199–212~~

~~[12] Emden van, P.H., “Accuracy of Wind Speed Data”, www.ekopower.nl~~

~~[13] Energizing Ohio’s Economy, Environment Ohio Research & Policy Center, 2007~~

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~~[14] Giles, R., “Lukasiewicz Logic and Fuzzy Set Theory”, Int. J. Man-Machine Studies, 1976, 8, 623 – 68~~

~~[15] Goh S.L., Popovic, D.H., & Mandic, D.P., “Complex-valued Estimation of Wind Profile and Wind Power” In: Proceedings of 12th IEEE Mediterranean Electrotechnical Conference; 2004. p. 1037–40.~~

~~[16] Green Energy Ohio (GEO), Wind Resource Assessment Report, 2009~~

~~[17] Hadipriono, F.C., & Sun, K.M., “Angular Fuzzy Set Models for Linguistics Values”, Journal of Civil Engineering Systems, 7(3): 148-156, 1990~~

~~[18] Hadipriono, F.C., “Assessment of Falsework Performance Using Fuzzy Set Concepts”, Elsevier Science Publishers B.V., Amsterdam, 1985~~

[19] Hadipriono, F., “Fuzzy Sets in Probabilistic Structural Mechanics”, in Probabilistic Structural Mechanics Handbook, Theory and Industrial Applications, Sundararajan, Ed., Chapman and Hall Publisher, New York, pp. 208 – 316, 1995.

~~[20] How Wind Turbines Work, http://www1.eere.energy.gov/windandhydro/~~

~~[21] Landberg, L., “Short-Term Prediction of the Power Production from Wind Farms”, Journal of Wind Engineering and Industrial Aerodynamics 1998; 80 (No. 1–2): 207–20.~~

[22] Li S, Wunsch, D.C., O’Hair, E., & Giesselmann, M.G., “Comparative Analysis of Regression and Artificial Neural Network Models for Wind Turbine Power Curve Estimation”, Journal of Solar Energy Engineering 2001; 123(No. 4):327–32.

~~[23] Manwell, J.F., McGowan, J.G., & Rogers, A.L., “Wind Energy Explained”, John Wiley & Sons Ltd, 2002~~

~~[24] Mamdani, E.H., “Application of Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis”, IEEE Transactions on Computers, Vol. C-26, No. 12, pp. 1182 – 1191, 1997~~

~~[25] National Climatic Data Center (NCDC), http://www.ncdc.noaa.gov/oa/ncdc~~

~~[26] Nfaoul, H., Buret, J. & Sayigh, A.A.M., “Stochastic Simulation of Hourly Average Wind Speed Sequence in Tangiers, (Morocco)” Solar Energy Vol. 56. No.3 pp 301 – 314, 1996~~

~~[27] Permitting of Wind Energy Facilities, Handbook Revision 2002~~

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~~[28] Rogers, A.L., Rogers, J.W. & Manwell, J.F. “Uncertainties in Results of Measure – Correlated –Predict Analysis”, AWEA, Denver, 2005~~

~~[29] Ross T.J., “Fuzzy Logic with Engineering Applications”, 2nd Ed., John Wiley & Sons Ltd, 2004~~

~~[30] Walker, J.F. & Jenkins, N., “Wind Energy Technology”, John Wiley & Sons Ltd, 1997~~

~~[31] Wind Energy Resource Atlas of United States, http://rrdc.gov/wind/pubs/atlas October, 1986~~

~~[32] Wiser, R and Kahn, E., "Alternative Wind Power Ownership Structures: Financing Terms and Project Costs", www.awea.org/faq/cost.html. 1996~~

~~[33] Zadeh, L.A., “Analysis of Complex System and Decision Processes”, http://www-bisc.cs.berkeley.edu/BISCProgram/default.htm, 1973~~

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~~APPENDIX A~~

~~INCIDENT LOG~~

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~~INCIDENT LOG~~

~~11/02/2007: Installation activities commenced. A few anemometers were temporarily connected. 11/03/2007: Anemometers disconnected to complete installation.~~

~~11/05/2007: Instruments reconnected improperly; temperature sensor not connected.~~

~~11/14/2007: Anemometers and wind vanes connected properly.~~

~~12/09/2007-12/11/2007: Data lost from all channels, possibly due to icing event; temperature data are also unavailable.~~

~~12/16/2007-12/18/2007: Data lost from all channels, possibly due to icing event; temperature data are also unavailable.~~

~~12/19/2007: Temperature sensor connected properly.~~

~~2/06/2008-2/08/2008: Data lost from all channels due to icing.~~

~~3/04/2008: Deadband/Null point adjusted on #7 wind vane at 100 degrees.~~

~~5/29/2008: Data card discovered by site sponsor to be missing; therefore, data was lost from 5/16/2008-5/28/2008.~~

~~7/21/2008: Site visit by GEO Staff; adjusted offset of channel 7 to zero.~~

~~7/21/2008: Data lost from channel 5 beginning at 4:20 p.m.~~

~~8/12/2008: Lost Channel 5 connection repaired at 11:20 a.m.~~

~~9/14/2008: Remnants of Hurricane Ike passed through Ohio; data omitted for affected period (7:10 a.m. – 11:50 p.m.).~~

12/10/2008-12/13/2008: Icing event affected channels 7-9; began at 3:40 a.m., channel 7 recovered at 4:00 a.m. on 12/13/2008, channel 8 recovered at 8:50 a.m. on 12/13/2008, and channel 9 recovered at 8:20 a.m. on 12/13/2008.

12/19/2008-12/24/2008: Icing event affected channels 1-9; began at 7:00 a.m., channels 1 & 2 recovered at 4:20 a.m. on 12/24/2008, channels 3 & 4 recovered at 3:20 a.m. on 12/24/2008, channel 5 recovered at 1:30 a.m. on 12/24/2008,

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~~channel 6 recovered at 2:00 a.m. on 12/24/2008, channel 7 recovered at 5:10 a.m. on 12/24/2008, channel 8 recovered at 4:20 a.m. on 12/24/2008, and channel 9 recovered at 3:10 a.m. on 12/24/2008.~~

~~12/25/2008: Icing event affected channels 7-8; began at 6:20 p.m. and ended at 8:30 p.m.~~

~~2/27/09: Data collection activities ended.~~

~~3/24/09: Equipment decommissioned from tower by GEO.~~

~~Courtesy: GEO 2009 (Use with permission for educational purposes)~~

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