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Nelson Mandela Metropolitan University

Doctoral Thesis

Optimisation of a Mini Horizontal Axis

Wind Turbine to Increase Energy Yield

During Short Duration Wind Variations

Author:

Sean Poole

Promoter:

Prof. RussellPhillips

Co-Promoter:

Dr. Frederik Vorster

A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy

in

Mechanical Engineering

March 2017

The financial assistance of the National Research Foundation (NRF) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at, are those of the author and are not necessarily to

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Declaration of Authorship

I, Sean Poole, declare that this thesis titled, ’Optimisation of a Mini Horizontal Axis Wind Turbine to Increase Energy Yield During Short Duration Wind Variations’ and the work presented in it are my own. I confirm that:

This work was done wholly or mainly while in candidature for a research degree

at this University.

Where any part of this thesis has previously been submitted for a degree or any

other qualification at this University or any other institution, this has been clearly stated.

Where I have consulted the published work of others, this is always clearly

at-tributed.

Where I have quoted from the work of others, the source is always given. With

the exception of such quotations, this thesis is entirely my own work.

I have acknowledged all main sources of help.

Where the thesis is based on work done by myself jointly with others, I have made

clear exactly what was done by others and what I have contributed myself.

Signed:

Date:

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“Special thanks to my wife for putting up with this engineer and supporting my further studies. Thanks also to go my God for His many blessings including this opportunity to research further into a topic I greatly enjoy.”

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NELSON MANDELA METROPOLITAN UNIVERSITY

Abstract

Engineering, the Built Environment and Information Technology Mechanical Engineering

Doctor of Philosophy

Optimisation of a Mini Horizontal Axis Wind Turbine to Increase Energy Yield During Short Duration Wind Variations

by Sean Poole

The typical methodology for analytically designing a wind turbine blade is by means of blade element momentum (BEM) theory, whereby the aerofoil angle of attack is op-timised to achieve a maximum lift-to-drag ratio. This research aims to show that an alternative optimisation methodology could yield better results, especially in gusty and turbulent wind conditions. This alternative method looks at increasing the aerofoil Reynolds number by increasing the aerofoil chord length. The increased Reynolds num-ber generally increases the effectiveness of the aerofoil which would result in a higher or similar lift-to-drag ratio (even at the decreased angle of attacked require to maintain the turbine thrust coefficient). The benefit of this design is a flatter power curve which causes the turbine to be less sensitive to fluctuating winds. Also, the turbine has more torque at startup, allowing for operatation in lower wind speeds. This research is as-sumed to only be applicable to small wind turbines which operated in a low Reynolds number regime (<500 000), where Reynolds number manipulation is most advantageous. The following papers have been written and presented and are based on the research of this document:

1. Rapid Prototyping of Small Wind Turbine Blades Using Additive Manufacturing (AppendixA) [1].

2. Optimization of a Mini H.A.W.T. Blade to Increase Energy Yield During Short Duration Wind Variations (AppendixB) [2].

The following poster was presented at the Small Wind Conference 2015 (Stevens Point, WI, USA) and is based on the research of this document:

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Acknowledgements

Further acknowledgements and thanks go to my promotors, Prof. Russell Phillips and Dr. Frederik Vorster, who have both supported me academically and financially. More importantly they have supported and guided me in such a way as to give me freedom in my research topics and research methods which has allowed me to follow my heart and learn more independently.

A special thanks to Etienne Phillips who has patiently guided and assisted me with the more technical electronic aspects of this research.

Thanks to Peter Freere for his constructive input into my research and assistance on electrical matters.

I would also like to acknowledge the financial support (scholarships) I have received during my studies:

• The NRF who has been my primary sponsor through their Renewable and Sus-tainable Energy Masters and Doctoral Scholarship.

• The NMMU through theirPost Graduate Research Scholarship programme.

• The Ernst & Ethel Eriksen Trust through their scholarship programme.

Thanks to Kestrel at Eveready for assisting and allowing me to use their outdoor wind tunnel for some of my testing.

Thanks to Riaan Opperman and eNtsa for scanning one of my blades, and also assisting with the erection of the two turbines at the ORF.

Thanks to Louwrens Kok, who through the RERG has assisted me with various me-chanical tasks including manufacturing the generator test bench mounting bracket and assistance with erecting and dismantling the two turbines at the ORF.

Finally, I would like to thank AMTC, and especially Karl du Preez, for the job I have held with them and the flexibility and financial support they have given me to prioritise and assist my research.

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Contents

Declaration of Authorship i Abstract iii Acknowledgements iv Contents v List of Figures ix

List of Tables xiii

Abbreviations xiv

Physical Constants xv

Symbols xvi

1 Background 1

1.1 Early History . . . 1

1.2 HAWT Blade Design Development . . . 6

2 Research Project Outline 8 2.1 Hypothesis . . . 8 2.2 Problem Statement . . . 8 2.3 Delimitations . . . 9 2.4 Significance of Research . . . 9 2.5 Research Methodology . . . 10 2.5.1 Literature Review . . . 10

2.5.2 LtDOD Blade Design . . . 11

2.5.3 RNOD Blade Design . . . 11

2.5.4 Calculate Analytical Results . . . 11

2.5.5 Build Designed Blades . . . 11

2.5.6 Build Experimental Platform . . . 11

2.5.7 Practically Test Blades . . . 11 v

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Contents vi

2.5.8 Discuss Results . . . 12

3 Introduction 13 3.1 Turbine Blades . . . 15

3.1.1 Lift-to-Drag Optimised Blade Design. . . 15

3.1.2 Reynolds Number Optimised Design . . . 16

3.2 Maximum Power Point Tracking (MPPT) Logic. . . 17

3.3 Yawing Mechanisms . . . 18

4 Literature Review 19 4.1 Small Scale Wind Turbines in General . . . 20

4.2 Blade Design . . . 20

4.2.1 General . . . 21

4.2.2 Reynolds Number Optimised Blade Design . . . 22

4.2.3 Conclusions . . . 23 4.3 MPPT Algorithm. . . 23 4.3.1 General . . . 23 4.3.2 Conclusions . . . 25 4.4 Yaw Mechanisms . . . 26 4.4.1 General . . . 26 4.4.2 Conclusions . . . 27

5 Adapted BEM Theory Model 28 5.1 BEM Theory . . . 28

5.1.1 BEM Theory Momentum Calculations . . . 29

5.1.2 BEM Theory Aerodynamic Calculations . . . 31

5.1.3 Validating the BEM Theory Model Calculations . . . 33

5.1.4 Prandtl Tip-loss Correction Factor . . . 34

5.1.5 Glauert Correction for High Values ofa . . . 36

5.1.6 Parasitic Drag of Blade Tips . . . 37

5.2 Optimised Blade Design Process . . . 38

5.3 Power Calculations Process . . . 38

6 Blade Design 41 6.1 NACA4412 Aerofoil Properties . . . 41

6.2 Lift-to-Drag Optimised Design (LtDOD) . . . 43

6.2.1 Theory . . . 43

6.2.2 Design . . . 44

6.3 Reynolds Number Optimised Design (RNOD) . . . 45

6.3.1 Theory . . . 45

6.3.2 Design . . . 49

6.4 Discussion . . . 51

7 Analytical Blade Analysis and Results 53 7.1 Static Power Curves . . . 56

7.2 Turbulence . . . 62

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Contents vii

8 Practical Setup and Testing Methods 68

8.1 General Equipment . . . 68

8.1.1 Wind Equipment Measurements . . . 68

8.1.2 Turbine Blade Prototyping . . . 69

8.1.3 Manufacturing Technique Geometry Verification . . . 70

8.1.4 Turbine Generator . . . 70

8.2 Testing Setups and Methods. . . 70

8.2.1 Outdoor Wind Tunnel Testing . . . 71

8.2.1.1 Test Setup . . . 71 8.2.1.2 Test Method . . . 73 8.2.1.3 Test Procedure . . . 75 8.2.2 In-field Testing . . . 75 8.2.2.1 Test Setup . . . 75 8.2.2.2 Test Method . . . 75

Turbine Rotational Speed and Angular Acceleration: . . . . 77

Turbine Generator Power: . . . 78

Kinetic Power Monitoring . . . 79

8.2.2.3 Test Procedure . . . 80

8.2.3 Portable Trailer Setup Testing . . . 80

8.2.3.1 Test Setup . . . 80

8.2.3.2 Test Method . . . 80

8.2.3.3 Test Procedure . . . 83

9 Practical Blade Analysis and Results 88 9.1 Outdoor Wind Tunnel Testing . . . 88

9.1.1 Turbine Startup . . . 90

9.1.1.1 Lift-to-Drag Optimised Design . . . 90

9.1.1.2 Reynolds Number Optimised Design . . . 91

9.1.2 Power Curves . . . 92

9.1.2.1 Lift-to-Drag Optimised Design . . . 92

9.1.2.2 Reynolds Number Optimised Design . . . 94

9.1.3 Conclusions . . . 97

9.2 In-Field Testing . . . 97

9.2.1 Wind Analysis . . . 98

9.2.2 Momentum Load Test . . . 103

9.2.3 Diode Load Test . . . 104

9.3 Trailer Testing . . . 105

9.3.1 Turbine Startup . . . 105

9.3.2 Power Curves . . . 106

9.3.2.1 Lift-to-Drag Optimised Design . . . 106

9.3.2.2 Reynolds Number Optimised Design . . . 108

9.3.3 Observations . . . 110

9.4 Discussion and Conclusions . . . 112

Startup Torque:. . . 113

Wind Measurements: . . . 113

Test Methods:. . . 113

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Contents viii

Observations . . . 113

10 Conclusions 114 10.1 Research Remarks . . . 115

10.2 Future Suggested Research . . . 115

A 2015 Prasa-RobMech Conference Paper 117 B Journal for New Generation Sciences (JNGS) 2016 Paper 124 C Small Wind Conference 2015 Poster 138 D Wind Measurements 139 D.1 Wind Measurements . . . 139

D.1.1 Sonic Anemometer Theory . . . 140

D.1.2 Hardware . . . 141

D.2 Wind Calculations . . . 141

E GinLong Generator Characterisation 144 E.1 Back EMF Component of Equivalent Circuit . . . 144

E.1.1 Back EMF Coefficient: . . . 146

E.1.2 Sinusoidal Waveform Validation: . . . 146

E.1.3 Phase Voltage Balance Check: . . . 147

E.1.4 Generator Friction and Windage Losses: . . . 147

E.2 Resistance Component of Equivalent Circuit. . . 148

E.3 Inductance Component of Equivalent Circuit . . . 148

E.4 Electrical Load Model Verification . . . 149

F Experimental Determination of the Mass Moment of Inertia 151 F.1 Analytical Calculations . . . 151

F.2 Testing of the Blade Sets . . . 152

F.2.1 Testing of the LtDOD Blade . . . 153

F.2.2 Testing of the RNOD Blade . . . 154

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List of Figures

1.1 Early Persian Windmill [3]. . . 1

1.2 Windmill [4]. . . 2

1.3 Wind Pump [5] . . . 3

1.4 12kW DC Wind Turbine Constructed by Brush [6] . . . 3

1.5 Jacobs 2.5kW Wind Turbine [7] . . . 4

1.6 1.25MW Putnam-Smith Wind Turbine [4] . . . 4

1.7 Darrieus Vertical Axis Wind Turbine [4] . . . 5

1.8 1.5MW, 64m Diameter Wind Turbine [8]. . . 5

1.9 Single Blade Wind Turbine [4] . . . 7

1.10 Two Blade Wind Turbine [4] . . . 7

3.1 TSR for a 3m Diameter HAWT Operating in Turbulent Conditions [2] . . 14

3.2 RERG Test Site . . . 15

3.3 Practical Results of a Constant and Variable Chord Tests at 4.5m/s [9] . 16 3.4 Power Curve Calculations from BEM Theory for 4 Blade Constant Chord vs 3 Blade Variable Chord . . . 17

4.1 Power Coefficient Variation with Yaw Angle and Axial Flow Factor [8] . . 26

5.1 A Single Control Volume and Element Section as Used in the BEM Model [2] 29 5.2 BEM Theroy Flow Boundary . . . 30

5.3 Induced Tangential Flow Wake (Exagerated) . . . 30

5.4 Aerofoil Vector Diagram [2] . . . 32

5.5 Wake-helicoidal Model to Account for Tip-losses . . . 35

5.6 Prandtl’s Wake-disc Model to Account for Tip-losses . . . 35

5.7 Glauert Correction Factor for High Values of a[8] . . . 37

5.8 Flowchart of BEM Theory for Calculating an Optimised Blade Design . . 39

5.9 Flowchart of BEM Theory for Calculating Power of a Wind Turbine for a Given Blade Element. . . 40

6.1 Lift Coefficient vs AoA at Various Reynolds Numbers for a NACA4412 Aerofoil (XFOIL) . . . 42

6.2 Drag Coefficient vs AoA at Various Reynolds Numbers for a NACA4412 Aerofoil (XFOIL) . . . 42

6.3 Lift-to-drag Ratios vs AoA at Various Reynolds Numbers for a NACA4412 Aerofoil (XFOIL) . . . 43

6.4 Lift-to-drag Ratios vs AoA at Various Reynolds Numbers for a NACA4412 Aerofoil (XFOIL) . . . 44

6.5 LabView Program Input Screen for Lift-to-Drag Optimised Design . . . . 45 ix

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List of Figures x

6.6 Lift-to-Drag Optimised Blade Parameters . . . 46

6.7 CAD Render of LtDOD Blade. . . 46

6.8 NACA4412 Airfoil Design Theory Areas . . . 47

6.9 Lift-to-drag Ratios vs AoA at Various Reynolds Numbers for a SG6041 Aerofoil (XFOIL) . . . 47

6.10 Lift-to-drag Ratios vs AoA at Various Reynolds Numbers for a S833 Aero-foil (XFOIL) . . . 48

6.11 Comparable Aerofoil Configurations . . . 49

6.12 RNOD Design Process Theory . . . 50

6.13 Lift-to-Drag Ratios vs AoA at Various Reynolds Numbers for a NACA4412 Aerofoil (XFOIL) Including Design Points at a -8 degree AoA Offset from the Maximum Lift-to-Drag Ratio AoA . . . 50

6.14 Reynolds Number Optimised Blade Parameters . . . 51

6.15 CAD Render of RNOD Blade . . . 51

6.16 Aerofoil Vector Diagram During a Gust . . . 52

7.1 Example of LabView Graphical Display for a Single Set of Parameters Showing Blade Power Density Along the Length of the Blade . . . 54

7.2 Example of LabView Graphical Display for a Single Set of Parameters Showing the Power Coefficient for Each Element Along the Length of the Blade . . . 55

7.3 Example of LabView Output Data for a Single Set of Parameters and All Blade Elements . . . 55

7.4 Example 2 of LabView Output Data for a Single Set of Parameters and All Blade Elements . . . 55

7.5 Power Curves for Lift-to-Drag Optimised Blade Design (Constant Wind Speed) . . . 56

7.6 Power Curves for Reynolds Number Optimised Blade Design (Constant Wind Speed) . . . 57

7.7 Power Curves for Lift-to-Drag Optimised Blade Design (Constant RPM) . 58 7.8 Power Curves for Reynolds Number Optimised Blade Design (Constant RPM) . . . 58

7.9 Maximum Power Point Curves . . . 59

7.10 Maximum Power Points (MPP) for RNOD and LtDOD Turbines at Var-ious Wind Speeds . . . 59

7.11 Comparative Averaged Power Curves (all data from 4 m/s to 10 m/s) . . 60

7.12 Power Curves for RNOD and LtDOD Turbines for a 50 m/s Wind . . . . 61

7.13 Power Blade Loading for the RNOD Turbine at No Load and 50 m/s Wind 61 7.14 Power Blade Loading for the LtDOD Turbine at No Load and 50 m/s Wind 62 7.15 Standard Deviation of Wind Speed according to NTM . . . 63

7.16 TI for Various Wind Speeds According to NTM. . . 64

7.17 Probability Distribution and Power Output for Varying Wind Speeds (Av-erage Wind Speed of 8m/s) . . . 65

7.18 Turbulence Corrected Power Curve for the LtDOD Turbine . . . 66

7.19 Turbulence Corrected Power Curve for the RNOD Turbine . . . 66

7.20 Energy Yield Differences due to NTM Corrections . . . 67

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List of Figures xi

8.2 LabView GUI of Live Wind Data . . . 69

8.3 Scan of the Front Side of the Manufactured RNOD Blade . . . 71

8.4 Scan of the Back Side of the Manufactured RNOD Blade . . . 72

8.5 Kestrel Outdoor Wind Tunnel Layout . . . 72

8.6 Setup of Power Logging Equipment for Outdoor Wind Tunnel Tests . . . 74

8.7 Example of LabView GUI Showing Live Power and Control Data . . . 74

8.8 ORF Test Site . . . 76

8.9 Setup of Power Logging Equipment for Field Tests . . . 76

8.10 Measurement of Wave Form Periods to Determine Change in Rotational Speed . . . 77

8.11 Diagram of 3-Phase Voltage and Current Sensors (VDR-Voltage Divider Resistor, SR-Shunt Resistor) . . . 77

8.12 Equivalent Circuit of Generator . . . 79

8.13 Portable Trailer Testing . . . 81

8.14 Setup of Load Cell to Measure Torque (G-Generator, LA-Lever Arm, LC-Load Cell). . . 82

8.15 Setup of Power Logging Equipment for Portable Trailer Tests . . . 83

8.16 Diode IV Curve for [10] . . . 84

8.17 Example of Load Curve Data Points from Trailer Setup . . . 85

8.18 Outliers of the Recorded Data Deviating from Power Curve . . . 85

8.19 Load Curve from Trailer Setup Showing Filtered and Unfiltered Results . 86 8.20 Example of Diode Bank Load Curves Used to Develop the Turbine Power Curve (RNOD Turbine at 5 m/s) . . . 87

9.1 Comparison of the Kestrel Outdoor Wind Tunnel and Natural Wind at the ORF (10 Hz Sample Rate) . . . 89

9.2 Comparison of Test Sites. . . 89

9.3 Lift-to-drag Optimised Turbine Startup Procedure . . . 90

9.4 LtDOD Blade Hub Extension . . . 91

9.5 Lift-to-drag Optimised Turbine with Blade Root Extensions Startup Pro-cedure . . . 91

9.6 Reynolds Number Optimised Turbine Startup Procedure . . . 92

9.7 Practical LtDOD Power Curves at Outdoor Wind Tunnel . . . 93

9.8 Normalised Practical LtDOD Power Curves at Outdoor Wind Tunnel . . 93

9.9 Normalised Practical LtDOD Power Curves at Outdoor Wind Tunnel Compared to Analytical Results. . . 94

9.10 Practical RNOD Power Curves at Outdoor Wind Tunnel. . . 95

9.11 Normalised Practical RNOD Power Curves at Outdoor Wind Tunnel . . . 95

9.12 Normalised Practical RNOD Power Curves at Outdoor Wind Tunnel Compared to Analytical Results. . . 96

9.13 Normalised Practical LtDOD and LtDOD Power Curves at Outdoor Wind Tunnel . . . 96

9.14 Logged Wind Speed at the ORF Test Site . . . 98

9.15 Logged Turbulence Intensity (TI) at the ORF Test Site . . . 99

9.16 Logged Gust Factor at the ORF Test Site . . . 99

9.17 Logged Wind Distribution and Weibull Distribution (λ= 7.4 and k= 3) at the ORF Test Site . . . 100

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List of Figures xii

9.19 Logged Wind Acceleration at the ORF Test Site (1 Second Averaged) . . 101

9.20 Logged Wind Vertical Angle at the ORF Test Site . . . 102

9.21 Logged Wind Heading at the ORF Test Site. . . 102

9.22 Difference Between Heading and 0.5 Second Averaged Heading . . . 103

9.23 Resistive Test Results with Kinetic Power Compensation (RNOD) . . . . 104

9.24 Diode Bank Test Results for Various Wind Speeds (Load Power Curve) . 105 9.25 Experimental Torque Curves for Stationary Turbines . . . 106

9.26 Practical Power Curves for the LtDOD Turbine for Various Wind Speeds 107 9.27 Practical Power Curves for the LtDOD Turbine . . . 107

9.28 Power Curves for the RNOD Turbine from Resistive Load Testing . . . . 108

9.29 Power Curves for the RNOD Turbine for Various Wind Speeds . . . 109

9.30 Power Curves for the RNOD Turbine. . . 110

9.31 Wind Variation Correlation to LtDOD Turbine Power Variation . . . 111

9.32 Wind Variation Correlation to RNOD Turbine Power Variation . . . 112

D.1 Gill 3D Sonic Anemometer [11] . . . 139

D.2 Flight Time Theory [12] . . . 140

D.3 Measured Components of Wind [12] . . . 141

E.1 Generator Test Setup . . . 145

E.2 Sample of Logged Data for Load Case 3 at 500 RPM . . . 145

E.3 Generator Back EMF vs Rotational Speed . . . 146

E.4 Sinusoidal Waveform of the Generator . . . 147

E.5 Torque vs RPM for Parasitic Generator Losses (Friction and Windage). . 148

E.6 Line-to-line Voltage and Current Measurements (Excited by a 9.46 kHz Sinusoidal Signal . . . 149

E.7 Measured Load Case Values Compared to Model . . . 150

E.8 Measured Torque Values Compared to Model . . . 150

F.1 Compound Pendulum to Determine the Radius of Gyration . . . 152

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List of Tables

7.1 Standard Deviation for Corrected Turbulence Model . . . 64

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Abbreviations

AoA Angle of Attack

BEM Blade ElementMomentum

DC Direct Current

EMF ElectroMotiveForce

FDM FusedDeposition Modeling

GF GustFactor

GUI GraphicUser Interface

HAWT Horizontal Axis WindTurbine

LtDOD Lift-to-DragOptimized Design

MPP MaximumPowerPoint

MPPT MaximumPowerPointTracking

NTM Normal Turbulence Model

ORB Optimum-Relation-Based

ORF OutdoorResearch Facility

P&O Perturb& Observed

PMG PermanentMagnetGenerator

PWM PulseWidth Modulated

RERG RenewableEnergyResearch Group

RMS Root MeanSquare

RNOD Reynolds NumberOptimized Design

RMS Root MeanSquare

RPM RevolutionsPerMinute

TI TurbulenceIntensity

TSR TipSpeed Ratio

VAWT VerticalAxisWind Turbine xiv

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Physical Constants

Density of Air (1 ATM, 15◦ C) [13] ρ = 1.225 kg/m3

Kinematic Viscosity of Air (1 ATM, 15◦ C) [13] ν = 1.46×10−5 m2/s

Resistivity of Copper (Cu) [14] ρ = 1.70×10−8 m

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Symbols

A area m2

a axial induction factor

-a0 tangential induction factor

-C chord length m CD coefficient of drag -CL coefficient of lift -CM coefficient of moment -CP coefficient of power -D drag N E EMF voltage V E energy J (or Wh)

EK rotational kinetic energy J

F force N

I current A

I mass moment of inertia kgm2

L lift N

L inductance H

M moment Nm

N number of blades

-n number generator poles

-P power W (J/s) PK rotational power W (J/s) R turbine radius m R electrical resistance Ω r local radius m xvi

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Symbols xvii

Re Reynolds number (chord based)

-T period s T torque Nm Tr local thrust N t time s (or h) Qr local torque Nm V velocity m/s V voltage V

α angle of attack deg

α rotational acceleration rad/s2

αr local angle of attack deg

β air flow angle deg

βr local air flow angle deg

γr local blade pitch deg

λ tip speed ratio (TSR)

-λr local speed ratio

-ω angular speed rad/s

ωRP M angular speed rev/min

ρ density kg/m3

φ diameter m

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Dedicated to the progress of small wind energy in South Africa.

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Chapter 1

Background

This chapter serves to provide a brief background primarily on Horizontal Axis Wind Turbines (HAWT). Although the early history of wind turbines provides little contri-bution to the academic research of today, it does provide a curious insight into the history of wind turbines, and perhaps an idea of where this technology may be heading. The early development of scientific procedures for designing wind turbines has also been included in this chapter.

1.1

Early History

The use of wind energy has been around for thousands of years in many different forms. The earliest example of a wind turbine performing work is a drag based vertical axis wind turbine as shown in Figure 1.1. These first windmills were constructed by the Persians and were used to grind grain and pump water about three thousand years ago [8,15,16]. Their construction was simple and involved vertical paddles consisting of a bunch reeds orientated around a central vertical axis. A gap in a barrier allowed the prevailing wind to apply a force on the paddles and therefore caused the turbine to rotate. This simple concept has been effective ever since, with some still in use today [15].

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Chapter 1. Background 2 The next notable development in wind energy technology came in the 10th or 11th century [17] with the European or “Dutch” windmill as shown in Figure1.2. This devel-opment was vastly different from the Persian windmill as this new windmill technology used a lift based horizontal axis concept. These windmills were used to grind grain, saw wood and pump water, and became an integral part of many European rural farming communities [8,15–18]. At their peak there were up to 100 000 functioning windmills in medieval Europe [18] with rotor diameters of up to 25m [16]. Although these machines were roughly 10 times less efficient than modern wind turbines [16], there was still an impressive total European installed capacity of 1500MW [16]. However, this phase of wind energy suffered a decrease in popularity since the development of the steam engine brought about an energy source which was controllable, portable, and cheap [16, 17]: controllable, since the steam engine could be throttled; portable, since coal could easily be transported to any location; and cheap, since the steam engine required a relatively low capital investment. These were all benefits that wind energy was not able to offer.

Figure 1.2: Windmill [4]

The next boom in commercial wind energy was that of the wind pump in the USA as shown in Figure1.3. This period saw about 6 million wind pumps installed between 1850 and 1970, mostly in the USA [8], with about 1 million still functioning around the world today [16]. Little has changed in their design over the last 100 years [16]. Historian, Walter Prescott Webb, said that the settlement of the Great Plains in the USA could be attributed to three reasons; the Colt 45, barbed wire, and the farm windmill [16]. The first fully self-regulated wind pump was patented by Daniel Halladay in 1854 [16,18] and was popular since their was little need for monitoring [16]. In the 1880s, Thomas Perry conducted scientific experiments on wind pump blades and developed designs which greatly increased their efficiency [16]. These designs incorporated curved metal sheets for blades and step down gearing; concepts which are still used in today’s wind pumps

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Chapter 1. Background 3 [16]. Widespread rural electrification in the 1930s was the cause of the decline of this technology [16].

Figure 1.3: Wind Pump [5]

From the late nineteenth century the first electricity generating wind turbines were devel-oped and researched. In Ohio, USA, during the year of 1888, Charles Brush constructed a 12kW DC windmill to charge batteries at his home, from which he powered lighting for his home (Figure1.4) [8,15,16].

Figure 1.4: 12kW DC Wind Turbine Constructed by Brush [6]

In Denmark, around the same time, research was conducted by Poul La Cour where he used wind turbines to create hydrogen and then burned the hydrogen to create light [15, 16]. Although these electricity generating turbines were based on adapted windmills of the time and did not start any trends, they did trigger a following of small electricity generating turbines [16]. The most notable of these small turbines being those pioneered by Marcellus Jacobs (Figure1.5) which could be accredited as the direct forerunner of the modern small wind turbine [16].

Another notable milestone in wind turbine development was that of the 53.3 meter diameter 1.25MW Putnam-Smith turbine installed at Grandpa’s Knob, Vermont, USA (1941) and shown in Figure 1.6 [8, 15]. This was the first turbine of mega-watt scale and was to be the only one ever developed of this scale for a further 40 years [8, 15]. The reason for this halted development of wind turbines was due to the enormous costs involved on such a project [8]. The Putnam-Smith turbine was ahead of its time for its size, pitch-control, and 2 blade configuration. Early blade failure in 1945 brought the project to an end [8,15]. Only the oil crisis in 1973 would re-ignite interest in large wind turbines due to the need for an alternative and secure energy source [8]. This new wave

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Chapter 1. Background 4

Figure 1.5: Jacobs 2.5kW Wind Turbine [7]

of interest was greatly benefited by new technological advancements already developed, namely: material sciences, computer sciences, aerodynamics, analytical design, analysis methods, testing and monitoring, and power electronics [15].

Figure 1.6: 1.25MW Putnam-Smith Wind Turbine [4]

After the 1970s oil crisis, and in light of negative public opinion of the dangers of nuclear power, the US government decided to invest in wind energy [15]. The US Department of Energy sponsored several projects from 100kW to 3.2MW in size [15]. Some of this research also investigated the use of 2 blades of which the Putnam-Smith turbine (Figure 1.6) could be considered the forefather [15]. Although no commercial projects were initiated from this research, much data was obtained [15].

Another push for wind energy came when the US federal government required all utilities to allow wind turbines to connect to the grid and then pay out the avoided costs related to the supplied energy [15]. Some states also initiated tax benefits for wind energy, of which California lead the way [15]. Many turbines were installed in California during this period including many Darrieus Vertical Axis Wind Turbines (VAWT) as shown in Figure 1.7. During the 1970s and 1980s much research was conducted on Darrieus turbines but they were found to be unreliable due to fatigue problems [15,16].

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Chapter 1. Background 5

Figure 1.7: Darrieus Vertical Axis Wind Turbine [4]

The modern wind turbines of today are mostly horizontal axis machines with 3 blades as shown in Figure 1.8. These machines range in size from a few inches in diameter to the colossal Vestas V164 8MW 164m diameter offshore wind turbines [19] with the trend towards even larger turbines in the future. The 3 blade configuration is considered more visually appealing [20] and is quieter than 2 blades [16], but it is also a balance between practicality, efficiency and costs, which all relate to cost effectiveness [16,21]. Almost all modern HAWT blades are constructed from composite fibre materials [16]. Also the blades on almost all modern HAWTs operate upwind of the tower [16]. On the larger turbines the nacelle is actively controlled to face the turbine blades into the wind, but on the smaller turbines, passive systems are used to control the yaw, generally by the use of a vertical tail fin [16].

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Chapter 1. Background 6

1.2

HAWT Blade Design Development

Not much analytical design went into the early wind turbines which were based on what seemed logical at that time in history (as can be seen by the 144 blades (Figure 1.4) on the turbine developed by Brush in 1888). But in the 18th century an Englishman by the name of John Smeaton conducted some scientific testing on wind turbines and established three simple rules which still apply to modern wind turbines [15]. These three basic rules are as follows:

• The speed of the blade tip is ideally proportioned to the speed of the wind (i.e. a specific turbine has an ideal Tip Speed Ratio1 (TSR)).

• The maximum torque of a turbine is proportional to the square of the wind speed.

• The maximum power of a turbine is proportional to the cube of the wind speed.

In 1888 the scientific work of Thomas Perry began to make an influence on wind turbine design (more specifically the wind pump design as shown in Figure 1.3). During the industrial revolution, Perry conducted over 5000 tests on different windmill designs using a steam-driven wind tunnel [16]. His research led to the scooped metal blades and reduction gearbox which are now common in many wind pumps. These improvements almost doubled the efficiency over the previous wooden slat design [16, 21]. Further improvements of the wind pump blade design were done by Flint and Walling with the introduction of their Star Zephyr windmill. They also did extensive wind tunnel testing and introduced twisted blades with rounded corners, and blade tapering which went from root to tip instead of the conceived ideal of tip to root [16].

In 1926, German scientist Albert Betz helped to formulate the Betz (or Betz-Joukowsky [20]) limit of 16/27 or 59.3% [8,15,17,20]. This limit represents the maximum theoret-ical energy which can be extracted from a wind and is the upper efficiency limit of any wind turbine. Then in the 1930s, Glauert and Betz developed the basis of the Blade Element Momentum (BEM) theory [15, 22] which combines the concepts of conserva-tion of mass, momentum, and energy [17], with blade aerodynamic theory. BEM theory forms the basis for most modern analytical calculations to design wind turbine blades or analyse their performance [8,15,17,18,20,23–25].

By 1931 patents for wind turbine airfoils were being issued [16] following the increasing scientific interest in wind turbines. Ulrich H¨utter, in the 1950s, applied modern aerody-namic principles to wind turbine design which contributed to the BEM theory [15].

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Chapter 1. Background 7 With new scientific analytical approaches, it was realised that only one blade was needed (Figure1.9) with a high Tip Speed Ratio (TSR) to effectively cover the swept area and minimise the wake rotation [16,21]. The increased TSR is also favourable to the high shaft speed required of most electrical generators. The main problems with a single blade turbine were the extra costs of the blade counter balance and increased noise due to high TSR [8].

Figure 1.9: Single Blade Wind Turbine [4]

Two blade designs as shown with this turbine from the 1980s in Figure 1.10 proved to be a better alternative to the single blade design [16]. Although two blade designs were slightly more efficient than single blade designs (10-14% [16]), they still had problems with dynamic imbalances during yawing. Although this problem could be solved with teetering blades, this would add extra complexity and cost.

Figure 1.10: Two Blade Wind Turbine [4]

Currently the most popular blade arrangement is a 3 blade design (Figure 1.8) with almost all HAWT manufacturers opting for this configuration. This configuration is a balance between cost, efficiency, simplicity, startup torque and practicality [16, 21]. Three blade turbines are also considered more visually appealing when compared to one or two bladed turbines [8,16,20].

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Chapter 2

Research Project Outline

This chapter serves to provide an outline and scope for the research project. Included in this section are the hypothesis, problem statement, delimitations, significance of re-search, and research methodology.

2.1

Hypothesis

A mini-scale Horizontal Axis Wind Turbine (HAWT) designed optimally according to BEM theory with an Angle of Attack (AoA) offset away from aerofoil maximum lift-to-drag ratio (decreased AoA) will yield a higher energy production when operating during short duration wind variations1.

2.2

Problem Statement

To optimise a mini-scale Horizontal Axis Wind Turbine (HAWT) for short term wind variations using the RNOD theory, the following problems will be looked at:

1. Design turbine blade according to Lift-to-Drag optimised Design (LtDOD). 2. Design turbine blade according to RNOD.

3. Analytically evaluate the designed blades.

4. Build designed blades (as defined in step 1 and 2).

1The turbine blade design using this hypothesis theory will be referred to asReynolds Number Opti-mised Design (RNOD)for the rest of this document.

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Chapter 2. Research Project Outline 9 5. Obtain experimental results.

6. Evaluate analytical and experimental results. 7. Discuss results relative to the proposed hypothesis.

2.3

Delimitations

The research will be limited by the following aspects:

1. No CFD analysis of the complete system (due to complexity of turbine air flow while including short term wind variations).

2. No cost analysis.

3. All designed turbines are to use a NACA 4412 aerofoil to provide consistency and fewer design variables.

4. No fatigue and long term reliability of the wind turbine systems.

5. No multiple in-field test sites (due to limited access to sites and time restrictions). 6. The turbine size will be limited to a diameter of 2 metres.

7. No optimisation of the turbine control system (MPPT logic). 8. No optimisation of yawing mechanism.

9. Power measurements are measured as accurately as possible within the budget and time.

10. The blade is assumed to not flex.

2.4

Significance of Research

On an academic level, the research challenges the conventional BEM method for design-ing a small wind turbine blade. The research looks at adaptdesign-ing the conventional BEM theory to design for increasing the Reynolds number instead of optimizing for lift-to-drag ratios.

On a practical level, South Africa has great potential for wind energy and on an utility scale this is already starting to be utilised [26–28]. However, the small wind energy sector is not growing at a similar rate and perhaps is getting worse with the onset of cheaper PV

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Chapter 2. Research Project Outline 10 technologies. This research aims to optimise small wind turbines operating in the built environment. If successful, this new design theory could help provide a more effective and more practical wind turbine, assisting small wind turbines in becoming more readily accepted and therefore leading to more installations in the built environment where the power is needed.

2.5

Research Methodology

The following steps are the proposed methodology to reach the desired outcomes for this research project (confirming if the performance of the RNOD turbine is more advanta-geous over the conventional LtDOD turbine).

2.5.1 Literature Review

The first step of the research methodology is an extensive literature study as completed and shown in Chapter 4. The purpose of this study is to better grasp the focus of the research, as well as the various aspects of wind turbine operation in order to ensure reliable test procedures and recorded data. The following topics were researched:

• Small scale wind turbines in general.

• Blade design.

• Wind conditions in the urban environment.

• Blade Element Momentum (BEM) theory.

• Aerofoil profiles.

• Maximum Power Point Tracking (MPPT) control theory.

• Turbine yaw control.

• Noise generation of wind turbines.

• Reynolds number optimised design theories.

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Chapter 2. Research Project Outline 11

2.5.2 LtDOD Blade Design

Create a conventional BEM design theory based program to design a turbine blade according to the Lift-to-Drag Optimised Design (LtDOD) process. This process is doc-umented in Chapter 6Section 6.2.2.

2.5.3 RNOD Blade Design

Create an adapted BEM design theory based program to design a turbine blade ac-cording to the Reynolds Number Optimised Design (RNOD) process. This process is documented in Chapter 6Section6.3.2.

2.5.4 Calculate Analytical Results

Use BEM theory to analytically generate the power curves of the RNOD and LtDOD blade designs. Evaluate these curves to determine the turbine characteristics and possi-ble advantages to the hypothesized RNOD design. This work is shown in Chapter 7.

2.5.5 Build Designed Blades

Use a practical and cost effective method to rapidly manufacture functional blades for the experimental testing of the 2 metre diameter turbines. This procedure was chosen to be additive manufacturing and is documented in Chapter8Section8.1.2and AppendixA.

2.5.6 Build Experimental Platform

Build an experimental platform in order to log turbine and wind data to develop the tur-bine power curves and determine the characteristics as shown in Chapter8, Section8.2.

2.5.7 Practically Test Blades

Obtain experimental results by practically testing the LtDOD and RNOD blade designs and logging turbine and wind data as shown in Chapter 9 Section 9.1 to Section 9.3. Compare these results to the analytical calculations.

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Chapter 2. Research Project Outline 12

2.5.8 Discuss Results

Discuss the results in relation to the proposed hypothesis as shown in Chapter 10. Confirm or deny the hypothesis.

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Chapter 3

Introduction

In order for a wind turbine to perform well it should be installed at a site with strong and consistent winds with the tower as high as feasibly possible (due to increased wind velocity with increase tower height [8,29–31]). This however is not always possible and the installer can be limited with site options as well as a limited proximity to where the energy is to be supplied. Perhaps one of the most challenging examples of these non-optimal sites is an urban environment (where tower height could also be restricted), but a rural site with rough terrain or complex topography could provide similar challenges. These complex terrains provide a challenging environment for wind energy generation because of the quality of the wind. These winds are generally found to have lower average wind speeds [25, 32, 33] and increased turbulence levels due to the increased surface roughness and increased thickness of the roughness sublayer region of the wind within the built environment [8,25,29,30,34].

To add to this problem of poor quality wind, a small scale wind turbine is usually designed based on Blade Element Momentum (BEM) theory with the goal of maximizing the aerodynamic lift-to-drag ratio of the turbine blades while maintaining the blade loading (axial forces) to optimise the flow of wind through the turbine [8,35–38]. This method of design optimises the efficiency of the turbine for a given wind speed and Tip Speed Ratio (TSR), but disregards the quality of the wind (it assumes a specific constant wind) as well as disregarding all other non-ideal operating conditions (it assumes a specific operating TSR).

These short falls in small wind turbine designs can be clearly seen in two reports con-ducted on numerous small-scale wind turbines mounted in urban locations [39, 40]. Of these reports, the Warwick Wind Trials Project showed an average capacity factor1 of

4.15% (excluding down time) from their 26 turbines tested over a period of a year [39].

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Chapter 3. Introduction 14 The other similar trial was also conducted in the UK by the Energy Saving Trust on small scale turbines operating in the urban environment. They suggested a less than 3% capacity factor1 for urban mounted turbines and directly compared this to similar tur-bines which were mounted in rural locations. The results suggest a 19% average capacity factor1 for the rural mounted turbines, which is six to seven times more than that of the urban located turbines [40]. Both these trials on commercially available small wind turbines suggest poor quality wind in urban environments and/or poor turbine design and calls for design optimisation of wind turbines for these specific conditions.

To further emphasize the difficult conditions that a small wind turbine can expect in a built environment; Figure3.1shows the the scatter of TSR’s for a commercially available small wind turbine system operating in strong winds. These results were obtained at the RERG (Renewable Energy Research Group) test site as shown in Figure 3.2(a)and Figure3.2(b). The data used for Figure3.1was from previous research [9]. This scatter shows that the turbine is seldom operating at or near its designed TSR and therefore a poor energy yield can be expected in gusty conditions.

Figure 3.1: TSR for a 3m Diameter HAWT Operating in Turbulent Conditions [2] The optimisation of a turbine to increase energy yield during short term wind variations lends itself well to urban wind energy production, and therefore will be the focus of this research in order for the research to maintain a commercial value. If a turbine is to operate well in an urban environment, then another major concern is noise, since

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Chapter 3. Introduction 15

(a) Site Layout (b) Aerial View

Figure 3.2: RERG Test Site

the close proximity to people will cause an audible annoyance [8, 33, 39, 41–54]. The scope of this research does not however cover any in depth research into noise reduction, although comments are made on the perceived noise levels during testing. The suggested research into optimisation of a wind turbine operating during short term wind variations (or within the built environment) will focus on adapting the BEM blade design theory. Although the topics of Maximum Power Point Tracking (MPPT) logic, and yawing mechanisms also effect energy production, these topics are not researched in depth, although they are considered in order to attempt to un-bias the test results. The scale of the tested turbines will be limited to a mini turbine (between 1.25m and 3m) as defined by Gipe [21].

3.1

Turbine Blades

Two BEM theory processes for blade design are compared during this research. The first is a conventional BEM theory process which optimises the design for maximum aerofoil lift-to-drag ratios (LtDOD turbine). This design is considered the benchmark for the results of the second original set of turbine blades (RNOD turbine). The RNOD turbine design process is a suggested continuation of the previous masters research conducted by Poole [9]. The one finding of this previous research (on a constant chord design) suggests an energy yield advantage in ‘oversizing’ the chord length on small wind turbines which is the basis of this research and the original instigation into the adaption of the BEM process.

3.1.1 Lift-to-Drag Optimised Blade Design

This blade design uses the conventional BEM method where the AoA of each section of the blade is optimised for the maximum aerofoil lift-to-drag ratio. This design process

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Chapter 3. Introduction 16 assumes that the most efficient gain in lift-to-drag ratio is obtained by adjusting the AoA, where the process of the next section (Section 3.1.2) assumes that the most efficient and practical gain of lift-to-drag ratios (for small wind turbines) is obtained through increasing the Reynolds number (ie. increasing the chord length).

3.1.2 Reynolds Number Optimised Design

A suggested topic for further research from a finding in Poole’s masters research is of a constant chord design [9]. The concept is to oversize the chord length of the turbine blades (relative to the lift-to-drag optimised design with a variable chord) and then optimise the pitch according to the BEM theory in order to maintain the optimum flow of wind through the turbine (to maintain optimum blade loading). As shown by the wind tunnel test results from Poole’s research in Figure 3.3, the constant chord design showed a flatter power curve when compared to the variable chord design2. Although the peak power was slightly less, a flatter power curve would better suite conditions where the operating TSR would constantly be fluctuating as in short duration wind variations. To prove that this practical finding was valid, Figure 3.4 shows a graph of basic BEM

Figure 3.3: Practical Results of a Constant and Variable Chord Tests at 4.5m/s [9] theory calculations done on both a lift-to-drag optimised design and a constant chord design (data from Poole [9]). Again these results show a flatter power curve with minimal decrease in peak power, which shows potential for this design operating in short duration

2for this graph the Variable Chord is representative of a lift-to-drag optimised design while the Constant Chord is more representative of a Reynolds number optimised design

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Chapter 3. Introduction 17 wind variations where the correct TSR is more difficult to maintain and a more forgiving power curve (flatter power curve) would be advantageous.

Figure 3.4: Power Curve Calculations from BEM Theory for 4 Blade Constant Chord vs 3 Blade Variable Chord

This new research looks at optimising this ‘oversized’ chord along the entire length of the blade and will not hold to the constant chord design as tested during Poole’s research [9]. The proposed method is somewhat similar to Poole’s process where he fixed an oversized chord and then optimised the AoA, only in this research the AoA offset (decrease) is fixed away from the maximum lift-to-drag ratio, and then the chord length is optimised in order to maintain the correct blade loading. Since the AoA is decreased, the chord length is increased in order to maintain optimal axial thrust. This method forces a larger Reynolds number through the increased chord length. The advantages of this design method are included in the scope of this research and the details of the design process shown in Chapter5, Section5.2.

3.2

Maximum Power Point Tracking (MPPT) Logic

A MPPT controls the electrical load on a wind turbine in an attempt to maximize the instantaneous power output. By varying the electrical load (via the MPPT unit) of the

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Chapter 3. Introduction 18 generator, the speed of the wind turbine can be controlled. For a specific wind speed and specific wind turbine, there will exist an optimal turbine speed which will allow the wind turbine to produce maximum power. The MPPT unit will attempt to find this maximum power turbine speed using one or some of various MPPT logics. This concept works well for fairly constant wind speeds which allow time for the MPPT units to search for or reach the optimum turbine speed, but the concept is flawed for gusty conditions [55–57]. During short duration wind fluctuations a wind turbine will not have sufficient time to reach an optimal speed (for the new wind speed) before the conditions have changed. This again highlights the need for a blade design concept to maximise energy yield during short duration wind variations.

3.3

Yawing Mechanisms

Yawing mechanisms attempt to orientate the wind turbine to face the wind. Small scale wind turbines are generally passively yawed. This mechanism is generally a vertical tail vane for a turbine with blades upwind of the turbine tower (See Figure 1.5). During short duration wind direction variations, the direction of the wind turbine would have to fluctuate rapidly in order to keep the turbine blades orientated into the wind. This once again highlights the need for a turbine blade which can accommodate some yaw offset where skewed air flow angles can be expected. Also an effective yawing mechanism would assist with better performance from the turbine blades, but the optimisation of a yawing mechanism is not included in the scope of this research.

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Chapter 4

Literature Review

This chapter looks at previously published research related to the topic of“Optimisation of a Mini Horizontal Axis Wind Turbine to Increase Energy Yield During Short Duration Wind Variations”. During this literature review it was found that there was relatively little research on small scale wind turbines [38] and most research on HAWTs was focused on large scale wind turbines as this is where most of the commercial value of the wind energy sector lies. This difference in commercial value can be seen in comparing world wide installed capacity of large and small scale wind turbines. The annual figures for newly installed capacity published by The World Wind Energy Association for 2011 is 38 000MW for large scale turbines [58] and 120MW for small scale turbines [59]. Generally large scale wind turbines will not be installed on a site with poor quality wind and so much of the research related to turbines operating during short duration wind variations focuses on small scale turbines. The rest of this chapter will look at a literature review of the optimisation of small scale wind turbines operating in poor quality wind and will focus on blade design, MPPT logic, and yawing mechanisms. Within each of these three main areas of research the emphasis will be placed on operation within the built environment, hence the following areas of research are also considered within each of the three main focus areas:

1. Optimisation to increase energy yield. 2. Low wind speed startup.

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Chapter 4. Literature Review 20

4.1

Small Scale Wind Turbines in General

In general, small wind turbines face many challenges. They are required to be self starting with blades set to a high AoA [49], they operate in a low aerofoil Reynolds number regime [49], they generally have poor yaw behaviour [49], and they require some form of over speed protection [49,60]. Walker also states that they generally have poor power output due to inaccurate power curves and wind measurements, and slow response times for the turbines and instruments [50]. Also wind turbines benefit very well from economy of scale and so a small wind turbine is disadvantaged by this fact and therefore needs to be as cost effective as possible [25].

Although small wind turbines will perform well in flat terrain [34, 40], many turbines are situated either in complex terrain or topology, or in the built environment. This provides a unique challenge for small wind turbines designs where the wind is obstructed (by buildings and other obstacles) and has a lower average speed [25, 32,33, 61], and is also turbulent and gusty [8,25,29,30, 34, 56,61,62]. This would require the focus of a turbine design to prioritize harnessing of the energy in the gusts as they contain more kinetic energy than the average wind speed [57]. Interestingly, for low wind speed, Lubitz found that an increase in turbulence led to an increase in energy production [56], which is logical as the addition of turbulence is an addition of energy to the wind. However, Devinant et al. states that turbulence decreases lift and increases drag of an aerofoil [63].

Various mathematical models exist to model and measure turbulence. A turbulence intensity factor is used as a measurement of the severity of the turbulence in the wind [8,

62,64]. Similarly a gust factor measures the intensity of wind gusts [8]. Various models to predict power output of a wind turbine in fluctuating wind conditions are: Albers estimation [62], Weibull estimation [8, 62], Rayleigh estimation (a special case of the Weibull estimation) [8,31], and Gaussian estimation [8,62].

4.2

Blade Design

The following sections document the literature findings on blade design for small wind turbines in general and also specifically on the proposed research of a Reynolds Num-ber Optimised Design (RNOD). A conclusion provides aspects of this literature review section which is applicable specifically to this research document.

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Chapter 4. Literature Review 21

4.2.1 General

BEM theory is the oldest and most commonly used analytical approach to wind turbine design [23–25,65]. Additional methods exist to compensate for limitations of the BEM theory, such as:

• Glauert correction factor [35,65–67].

• Prandtl tip-loss correction factor [8,35,37,66,67].

• Tip induced drag [68].

CFD remains the alternative to BEM theory but is computationally expensive [23]. One of the areas of importance for an urban wind turbine is limited noise pollution created by the turbine. For a turbine blade the following areas of noise have been suggested [8,44,46,69,70]:

• Leading edge inflow turbulence noise.

• Blunt trailing edge noise.

• Blade tip noise.

• Flow separation noise.

• Turbulent boundary layer trailing edge noise.

• Laminar boundary layer vortex-shedding noise.

To solve the problem of noise, some solutions have been researched. Lee et al. have optimised an aerofoil profile to minimise noise [71]. Noise has also been found to increase with turbulence [45], and so could be more of a problem in the built environment. Kimet al. suggests that a reduction in angle of attack leads to decreased broadband noise [8,44]. Increased TSR ratio is shown to increase noise intensity [8,17,72,73] and may also lead to increased vibration issues which can cause problems with roof mounted turbines [25]. Another aspect of small scale wind turbine design is the fact that they need to operate in less than average wind conditions [20,25,32,33]. This requires more emphasis on the need for good low wind speed starting behavior and low wind speed operation [53,74]. Small wind turbines face several challenges during startup, namely: very low Reynolds numbers due to small scale and low wind speeds [20, 32, 38, 49, 53, 61, 74, 75], a high blade AoA, since very few small wind turbines have pitch control [32,74,76], the

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Chapter 4. Literature Review 22 requirement of startup by wind alone [76], and also unsteady wind conditions usually associated with small wind turbines [32, 74]. A turbine operating in turbulent and gusty conditions may have some advantages as Ebert suggests that gusts assist turbines at startup [32]. Various research has been shown to provide solutions for low wind speed start up and is mentioned in the following:

Leung et al. and Li et al. state that increase blade solidity leads to increased torque which is what is required at startup [25,77].

Singh & Ahmed and Wright & Wood state that the starting torque of a turbine blade is generated at the blade root while operating power is generated at the tip [53,74]. This suggests that the blade root should be optimised to assist with the starting torque. Mayeret al. state that a high blade pitch causes a long idling time during startup and increasing the blade pitch will assist in a more rapid start [76].

A good aerofoil design is required for startup and operation at low Reynolds numbers [53], and several aerofoil designs and design methods have been suggested for a low Reynolds number wind turbine blade [61,75,78].

Sicot et al. shows that a thicker leading edge of an aerofoil will produce more post stall lift [8,79], which is the condition at startup due to the extremely high AoA.

Singh & Ahmed suggest the use of trip wires or turbulators to promote the transition from a laminar to turbulent boundary layer [53]. This is helpful at low Reynolds numbers since it eliminates laminar separation bubbles and delays separation from the leeward surface at a high AoA [8,53].

Singh & Ahmed also suggest some practical advice, that lighter blades have a lower inertia and require less energy to accelerate therefore assisting with startup and reaction time during fluctuating winds [53]. They also suggest that material costs are generally insignificant for small wind turbines [53] and therefore (to a degree) less emphasis should be placed on blade number and blade size (solidity).

Two more observations which may prove to be useful during research are: the use of stall flags on the leeward side of the blade to identify flow separation during operation [65], and the pressure distributions on a linear and rotating aerofoil are very similar [79] which supports the use of 2D aerofoil properties in the BEM theory.

4.2.2 Reynolds Number Optimised Blade Design

The common BEM theory design process ensures that the blade AoA is selected to generate the maximum lift-to-drag ratio in order to optimise the design. This design method is common practice [8, 35–38], but does not ensure maximum power yield as the method only optimises for one specific point (at a given design wind speed and TSR) [69,80]. The wind turbine design process is generally not optimised for short term

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Chapter 4. Literature Review 23 wind variations although research has gone into looking at increasing blade efficiency while operating in the wake of another wind turbine [8,31,63,65].

Various fixed blade HAWTs have been designed for low wind speeds. Kishore & Priya designed their SWEPT wind turbine for a rated wind speed of 4 m/s [38].

Hiraharaet al. designed theµF500 which has blades with a low aspect ratio [33]. A low aspect ratio blade has good performance for a wide range of AoA [33] and therefore is a good design to operate effectively during rapid wind fluctuations or turbulence. Grant et al. has conducted research on ducted turbines in the built environment and has modelled the wind flow around buildings [48].

No research was found on adapting the BEM process by offsetting the AoA (decreasing) away from the maximum lift-to-drag ratio in order to increase chord length and therefore increase the Reynolds number. This research is therefore considered a novel approach for the“Optimisation of a Mini Horizontal Axis Wind Turbine to Increase Energy Yield During Short Duration Wind Variations”.

4.2.3 Conclusions

The blade design for an urban small HAWT is most likely to have a low TSR. This will minimize noise and vibrations and ensure a higher blade solidarity which would assist startup torque. If the turbine blades could run at a lower AoA, this could also decrease noise. A lower TSR will also increase blade pitch which will assist with startup torque. The possibility of trip wires or turbulators could also be investigated to increase low Reynolds number performance and also minimize separation noise.

4.3

MPPT Algorithm

The following section reviews the literature found on MPPT logics. The Conclusions section suggests the application to this PhD research.

4.3.1 General

Various MPPT algorithms have been suggested, but generally all of these can be grouped into three main classes. These classes include Tip Speed Ratio (TSR) control, Optimum-Relation-Based (ORB) control, and perturb and observe (P&O) control [55,81–83]. The TSR control relates a measured wind speed to a predetermined turbine shaft speed which would ensure maximum power production. This method requires an accurate form of wind measurement as well as an accurate database of maximum power points for all

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Chapter 4. Literature Review 24 wind speeds. Although this method is simple and intuitive, its main drawbacks are the need for extra wind measuring instruments, and that the changing characteristics of an ageing turbine would not be accounted for.

The ORB control method looks at measurable outputs of the wind turbine and relates these to a predetermined database in order to generate an output goal. The most common measurables are electrical power versus shaft speed or electrical power versus electrical current [55]. The ORB algorithm performs well dynamically, and is also simple to implement as it does not require additional instruments to measure the wind speed. The shortfalls of the ORB method are the requirement of an accurate database of the system characteristics (which would require extensive lab tests) and also, as with the TSR control method, this system does not allow for the changing characteristics of an ageing wind turbine system. To overcome this problem of changing characteristics of an ageing system, the system would require periodic lab tests in order to keep the ORB algorithm database up to date [55].

The last algorithm, the P&O method, is based on an algorithm which initiates a per-turbation1 (which would allow the turbine to either speed up or slow down), and then

monitors the effect that this perturbation has on the power output of the wind turbine system. If the perturbation increases the power output, then the algorithm continues with this perturbation in order to continue increasing the output power. If the per-turbation decreases the power output, then the algorithm will introduce a perper-turbation opposite to the initial perturbation in order to increase power output. The main bene-fits of this algorithm is that it does not require any knowledge of the characteristics of the wind turbine system. Also any changes to the characteristics of the wind turbine system, such as an ageing system or changes in air density, will automatically be ac-counted for in the nature of this algorithm. The disadvantages of this system is that the P&O algorithm requires a period to search for the Maximum Power Point (MPP) and therefore this lag in response is not well suited for dynamic conditions (short term wind variations) [55–57]. This lag response is also due to the turbine inertia which creates a low-pass filter for any fluctuating wind conditions [83].

Out of the three classes of MPPT algorithms, the P&O method seems to be the best because of the simplicity of implementation and its independence from an accurate database of the characteristics of the wind turbine. Also this method can adapt to a varying wind quality and a varying air density [83]. But since this method is the worst suited of the three MPPT classes for dynamic wind conditions [83], this algorithm would need to be optimised further. The following research relates to MPPT algorithms specif-ically for gusty winds and the P&O method:

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Chapter 4. Literature Review 25 Dalalaet al. suggest to use the rate of change of the DC-link voltage to define an adap-tive step size in the P&O algorithm in order to control the rate at which the MPPT converges with the MPP [55]. This would allow a quicker response to dynamic changes in the wind.

Diaz-Guerra et al. show that for a large scale turbine it could be beneficial to decrease blade loading in order to prevent a “peaked” Cp value and therefore prevent blade

stalling which results in decreased performance [80]. This research shows that aiming for the instantaneous maximum power will not necessarily produce maximum yield dur-ing turbulent conditions.

Huiet al. and Putruset al. have shown that a fuzzy logic controller could react quicker during fluctuating winds when compared to a conventional P&O MPPT [81,84]. Seraet al. also suggest an improvement on the P&O method called the dP-P&O Method. Although Sera applies the MPPT algorithm to photo-voltaic technology, the logic is ap-plicable to wind energy. This dP-P&O method attempts to separate the effect of the perturbation from the changing conditions in order to observe the changing conditions more accurately and rapidly [85].

Kortabarria presents an Advanced Perturb and Observe Torque Control (APOTC) method which is a variation of the P&O method and is shown to react about 150% quicker during fluctuating wind conditions [82].

Since MPPTs are generally considered to react too slow for rapid wind variations [50, 56, 57, 82], Bystryk & Sullivan suggest the use of the wind turbine generator as a motor to speed up the turbine in order to reach the MPP quicker and make better use of the energy in a gust [57]. Bystryk & Sullivan also mention the fixed voltage method (constant turbine speed) to be very simple but show it to be the least effective [57]. This constant speed method may be preferable if the turbine blade is designed for a wide range of TSRs, therefore somewhat eradicating the need of a rapid response time or a complex control system.

4.3.2 Conclusions

Although there exists a fair amount of research on MPPT algorithms there seems to be a niche for a P&O algorithm with the inclusion of rotating inertia. This system would allow for more accurate power predictions since the energy captured by the turbine blades is transfered not only into electrical energy, but also into kinetic energy in the rotating system (blades, hub, generator) and therefore should be taken into consideration. Another option for a MPPT algorithm would be a simple constant speed control. This would only be viable with a turbine blade designed specifically for these conditions. The benefits of this type of system would be low centrifugal loads, less noise since the tip

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Chapter 4. Literature Review 26 speeds are lower [72], less vibrations for a roof mounted system [25], and a simple MPPT algorithm (or no active control).

4.4

Yaw Mechanisms

The following section documents the literature found on small wind turbine yawing mechisms. This is followed by a conclusion which suggests the application to this PhD research.

4.4.1 General

Passive yaw mechanisms are most common on small scale wind turbines [20, 86], but there seems to be very little research done on passive yaw mechanisms in general [86]. Wind conditions in an urban environment are directionally unstable [25,86] and a tur-bine operating in these conditions is likely to be in some state of yaw [56]. A yawed turbine will not produce maximum power [49, 86], and a suggested power correction factor of Cos2θ should be applied to the expected power output [8, 20]. Therefore a yawing mechanism needs to be optimised to ensure maximum energy yield from the wind turbine. From Figure4.1it is shown that a 10 degree yaw seems acceptable while a 20 degree yaw starts to show a significant power drop. Although passively yawed tailed wind turbines are unstable [86], the difficulty with increasing tail size to force the turbine to yaw quicker is that this causes large gyroscopic forces on the blades [72] which causes blade fatigue [86]. Wood suggests a tailed design not to follow high frequency directional fluctuations but only the low frequency changes [20].

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Chapter 4. Literature Review 27

4.4.2 Conclusions

Although having a wind turbine well orientated into the wind is advantageous, quick yawing actions will cause unwanted forces on the blades. Another problem of urban wind conditions is the large vertical component of wind [72] which cannot be tracked by a horizontal yawing mechanism. Therefore the issue of skewed air flow through a wind turbine is perhaps better solved through optimised blade design to suite these unavoidable yawed or skewed air flow conditions. Also, a turbine running at a lower TSR could allow for a more rapid yawing action as the gyroscopic forces will be less, therefore requiring less of a yawing moment while also causing less fatigue loading on the blades.

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Chapter 5

Adapted BEM Theory Model

This chapter documents the adapted Blade Element Momentum (BEM) theory used in this research. This chapter includes corrections to the BEM theory (Prandtl tip-loss correction factor, Glauert correction for high values ofa, and parasitic drag of blade tips, as well as the adaptions to the BEM theory used to design the RNOD turbine blades). The first section of this chapter shows the principal calculations used for the general BEM theory. The following two sub-sections show the details of the BEM process used to design the optimised wind turbine blades (LtDOD and RNOD), as well as the details of the BEM process used to analytically determine the power output for the designed turbine blades (LtDOD and RNOD).

5.1

BEM Theory

The fundamentals of the BEM theory ensure that the forces required to change the momentum of the moving air by absorbing kinetic energy (Section 5.1.1) are equal to the aerodynamic forces applied by the turbine blades on the air (Section 5.1.2). Both sets of forces are dependant on the other but must be equal in order of the BEM theory model to apply (Section 5.1.3). Another requirement of the BEM theory is the division of the turbine blade into elements along its length as shown for a single element in Figure 5.1. Each element section represents a control volume of wind flowing through the turbine as well as a section of the turbine blades which interacts with that control volume.

The assumptions made by the BEM theory are as follows:

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Chapter 5. Adapted BEM Theory Model 29

Figure 5.1: A Single Control Volume and Element Section as Used in the BEM Model [2]

2. The flow over the aerofoil (turbine blade) is considered to follow along a consistent radius relative to the turbine axis, and therefore 2D aerofoil properties are adequate to calculate aerodynamic forces.

3. The aerodynamic forces applied for each element are uniformly distributed along the swept area.

5.1.1 BEM Theory Momentum Calculations

In the air momentum part of the BEM theory, the wind turbine is considered a permeable disc through which the wind passes. This disc absorbs the kinetic energy of the wind and therefore decreases the velocity of the air. Half of the total decrease in wind speed is assumed to occur before the wind turbine and the other half downstream of the wind turbine [8]. The air velocity in the axial direction decreases from the free stream wind velocity (Vwind) by an axial induction factoraby the time it reaches the turbine blades

and then again by the same factor downstream of the turbine. Figure 5.2 shows this decrease in axial air velocity and the expanding flow boundary as the velocity decreases and the mass flow rate remains constant. Similarly the torque of the turbine induces a tangential velocity to the air opposite in direction to that of the blade (Figure 5.3). This tangential velocity is assumed to be half initiated before the wind turbine and half downstream of the turbine [8], similar to the axial velocity change. The magnitude of the tangential component of the ai

References

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