An Assessment of Wind Loading and Wind Energy Potential for Sri Lanka

AN ASSESSMENT OF WIND LOADING AND WIND ENERGY POTENTIAL FOR SRI LANKA

Weihena Liyanage Sanjaya Maduranga

(168004K)

Degree of Master of Philosophy

Department of Civil Engineering

University of Moratuwa Sri Lanka

June 2019

AN ASSESSMENT OF WIND LOADING AND WIND ENERGY POTENTIAL FOR SRI LANKA

Weihena Liyanage Sanjaya Maduranga

(168004K)

Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Philosophy

Department of Civil Engineering

University of Moratuwa Sri Lanka

June 2019

DECLARATION OF THE CANDIDATE & SUPERVISOR

I declare that this is my own work and this dissertation does not incorporate without acknowledgement any material previously submitted for a Degree or Diploma in any other University or institute of higher learning and to the best of my knowledge and belief it does not contain any material previously published or written by another person except where the acknowledgement is made in the text.

Also, I hereby grant to University of Moratuwa the non-exclusive right to reproduce and distribute my dissertation, in whole or in part in print, electronic or other medium. I retain the right to use this content in whole or part in future works (such as articles or books).

Signature: …………………… Date: …………………

The above candidate has carried out research for the MPhil dissertation under my supervision.

Signature of the supervisor: …………………… Date: …………………

DEDICATION

I will dedicate this dissertation to Dr. C. S. Lewangamage, my supervisor and mentor, as the person who always encouraged me to complete this study successfully.

W. L. S. Maduranga, Department of Civil Engineering, University of Moratuwa. 24.06.2019

ACKNOWLEDGEMENTS

There are number of people and institutions whom I need to pay my gratitude for their help towards the successful completion of this study.

I am especially indebted to Dr. C. S. Lewangamage, Senior Lecturer of the Department of Civil Engineering, University of Moratuwa, who supervised and guided me throughout the whole period of the study and who provided me the academic environment necessary to pursue my research goals. Also his guidance helped me to won Professor Raghu Chandrakeerthi award for the Best Paper (gold medal) in SSESL (Society of Structural Engineers, Sri Lanka) Annual Sessions, 2017. Furthermore, his endless efforts helped me to include the findings of this study in Sri Lankan National Annex for Euro Code 1 which is published by Sri Lankan Standard Institution.

This research work could not have been possible without the financial support provided by the SRC (Senate Research Council) of University of Moratuwa under the grant number SRC/LT/2016/01. Also I wish to thank the immense support given by the Department of Civil Engineering, University of Moratuwa and its academic and non-academic staff members. Prof. J. M. S. J. Bandara, the Head of the Department was always kind enough to give all the administrative support whenever necessary.

Further I would like to express my gratitude to my research progress committee members including Prof. M. T. R. Jayasinghe (Chairman of the panel), Prof. A. A. D. A. J. Perera (Research coordinator), Prof. R. U. Halwatura (Research coordinator), Dr. K. S. Wanniarachchi (External panel member), Dr. W. D. A. S. Rodrigo (Member from the Higher Degree Committee), who provided me an extensive personal and professional guidance to improve my research findings.

Dr. M. Narayana, Senior Lecturer, Department of Chemical Engineering, University of Moratuwa guided me by providing some basic knowledge in wind energy

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forecasting. Also he gave the permission to use the licensed version of WAsP 11 software package available in the Department of Chemical Engineering.

I wish to express my special gratitude to the Department of Meteorology of Sri Lanka and its staff members who supported me to acquire the wind data required for this study free of charges, especially Mrs. Anusha Warnasooriya, Mr. Vajira Lokuhetty and Mr. Asanga Priyadarshana. Further I would like to express my gratitude to SLSEA (Sri Lanka Sustainable Energy Authority) for providing the guidance and wind data required for this study free of charges. Also the support given by Prof. K. D. W. Nandalal by providing some of the wind data is acknowledged. I would also like to thank Mr. Lasith Wimalasena, the chief operating officer of WindForce (Pvt) Ltd. for the support given by arranging several field visits in their operating wind farms and for providing the necessary details.

I am also grateful to all of those with whom I have had the pleasure to work during this study including Mr. P. H. Alwis and Mr. P. A. A. Chathuranga both who were final year undergraduate students during the period of 2016/2017.

Finally, I must thank to all the people who helped me in many ways throughout the period of the study.

W. L. S. Maduranga, Department of Civil Engineering, University of Moratuwa. 24.06.2019

ABSTRACT

It was more than 40 years ago that Sri Lanka last established a wind loading map after the severe cyclone that struck the country in 1978. It is strongly believed that statistical methods had not been used in developing this wind loading map. Hence, the map can either overestimate or underestimate the wind speeds at least in some of the regions of the country. Therefore, an updated map which suits the changing climate patterns experienced in the country has become a necessity. In Sri Lanka, different wind codes are being used when structures are designed to withstand wind actions. Moreover, there is no suitable wind loading map that can be used with the Eurocode 1 or BS 6399-2.

The existing wind resource maps for Sri Lanka have been developed in macro scales with low resolutions which is not adequate for effective decision making in wind power generation. Moreover, most of them represents wind speed distributions except for wind power distribution. Therefore, the industry always uses expensive methods to identify the suitable regions for the establishment of wind turbines.

As the initial stage of this study a wind loading map for Sri Lanka was developed for different return periods (5, 10, 50, 100, 200, 500 and 1000 years) and for different averaging time periods (3-second gust, 10-minute average and hourly mean) using the wind data obtained from 24 weather stations. The data used were the monthly maximums of 3-minute average and instantaneous maximum wind speeds, recorded over a period of about 35 years. Extreme value distributions called Gringorten and Gumbel methods were tested to predict the extreme wind speeds. Finally, the Gringorten methods was adopted due to its unbiased nature. The generated wind contours for both 3-second gust and 10-minute average basic wind speeds were analyzed for defining the wind loading zones for Sri Lanka.

Altogether a new wind power distribution map was proposed for Jaffna Peninsula region in Sri Lanka which has been previously identified as a region with a higher wind energy potential. The required data was obtained from SLSEA (Sri Lanka Sustainable Energy Authority) and the Survey Department of Sri Lanka. Computational Fluid Dynamics based model has been used for the generation of wind power distribution m a p . The resolution of the map has been increased up to 150 m x 150 m (5” x 5”). Coastal regions such as Veravil, Pooneryn, Ampan, Punkudutivu, Kayts, Kankesanturai, Ponnalli Khadu, Karainagar, Mandaitivu and Alvai were identified as the regions which have the highest wind energy potential in Jaffna Peninsula.

Keywords: Wind loading map for Sri Lanka, Wind loading zones, Basic wind speed, Gringorten method, Wind energy forecasting, Wind power distribution, Siting of wind farms, Jaffna Peninsula

TABLE OF CONTENTS

Declaration of the Candidate & Supervisor i Dedication ii Acknowledgements iii Abstract v Table of Contents vi List of Figures ix List of Tables xiii List of Abbreviations xv List of Symbols xvi List of Appendices xviii 1. Introduction 1 1.1 Background 1 1.1.1 Adequacy of existing wind loading map for Sri Lanka 1 1.1.2 Adequacy of existing wind resource maps for Sri Lanka 4 1.2 Scope of the Study 7 1.3 Objectives 8 1.4 Outcomes 8 1.5 Arrangement of the Dissertation 9 2. Literature Review 10 2.1 Different Standards Which are used to Calculate the Wind Actions 10 on Structures 2.2 Attempts towards Wind Zone Demarcation for Several Countries 11 2.2.1 An attempt towards wind zone demarcation for Sri Lanka 11 2.2.2 Maximum wind hazard map for Sri Lanka developed by 14 DMC 2.2.3 Development of basic wind speed map for Oman 16 2.2.4 Development of basic wind speed map for India 18 2.2.5 Development of wind zone map for Germany 20 2.3 Conversion between Gust Speeds for Different Averaging Periods 22

2.4 Probabilistic Methods of Predicting Extreme Wind Speeds 26 2.4.1 Gumbel distribution 26 2.4.2 Gringorten distribution 27 2.4.3 Generalized extreme value distribution 28 2.5 Attempts towards Demarcating Wind Energy Resource Atlases 29 for Sri Lanka 2.5.1 Wind energy resource atlas of Sri Lanka and the 29 Maldives developed by NREL 2.5.2 Wind resource atlas of Sri Lanka developed by AWS 34 Truepower, LLC 2.5.3 Wind resource atlas of Sri Lanka developed by Vortex 36 2.5.4 Average wind hazard map of Sri Lanka developed by 38 DMC 2.5.5 Solar and wind resource assessment in Sri Lanka by 40 NERDC 2.6 WAsP (Wind Atlas Analysis and Application Program) 40 2.7 Several Approaches Made towards Wind Energy Forecasting 41 2.8 Some Important Relationships in Wind Engineering 43 2.9 Summary of the Chapter 45 3. Data Collection 48 3.1 Wind Data Available in the Department of Meteorology, Sri 48 Lanka 3.2 Wind Data Available in SLSEA 51 3.3 Geographical Maps Available in the Survey Department of Sri 54 Lanka 3.4 Summary of the Chapter 55 4. Development of Wind Loading map 56 4.1 Methodology 56 4.2 Kriging Interpolation for a Two Dimensional Spatial System 57 4.3 Conversion between 3-Second Gust and the Other Averaging 57 Periods 4.4 Determination of Extreme Wind Speeds 60

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4.5 Generating of the Wind Loading Map 66 4.6 Proposed Modification to the Generated Map 73 4.7 Summary of the Chapter 74 5. Development of Wind Power Distribution Map 76 5.1 Methodology 76 5.2 Identifying a Suitable Region for the Study 77 5.3 Procedure to Generate the Wind Power Distribution Map Using 77 WAsP 5.3.1 Basic principles and mathematical models used in WAsP 81 5.3.2 Feeding the data to the software 82 5.3.3 Defining the coordinates 83 5.3.4 Modelling the topography of the region 85 5.3.4.1 Surface roughness 85 5.3.4.2 Orography 86 5.3.4.3 Sheltering effect 86 5.3.5 Defining roughness characteristics in WAsP 86 5.3.6 Feeding the wind data to WAsP 88 5.4 Generation of Maps 88 5.5 Comparison of the Results 91 5.6 Summary of the Chapter 94 6. Conclusions and Recommendations 97 6.1 Conclusions 97 6.2 Recommendations 98 Reference List 100 Appendix – A: Positioning of Weather Measuring Instruments in the 103 Wind Masts Appendix – B: Graphs Generated by Applying the Gringorten and 106 Gumbel Methods for the Weather Stations in Sri Lanka Appendix – C: Graphs Generated to Visualize the Monthly Variations in 130 Wind for Northern Region in Sri Lanka Vita 132

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LIST OF FIGURES

Page

Figure 1.1 Existing wind loading zones for Sri Lanka 2 Figure 2.1 Wind loading zones proposed by Nandalal and Abeyruwan 12 Figure 2.2 Tracks of past cyclones and storms through Sri Lanka 13 Figure 2.3 Maximum wind hazard map of Sri Lanka developed by DMC 15 Figure 2.4 Basic wind speeds for Oman which was determined by 17 Alnuaimi et al. Figure 2.5 Basic wind speed map for India suggested by Lakshmanan et al. 19 Figure 2.6 Wind zone map for Germany proposed by Kasperski 21 Figure 2.7 Comparison between instantaneous maximum and 3-second 22 gust

Figure 2.8 Roughness lengths (푍표) for different types of surfaces 24 Figure 2.9 Wind energy resource map of Sri Lanka developed by NREL 30 Figure 2.10 Wind electric potential of Sri Lanka based on districts (Areas 31 with a wind power density more than 300 Wm-2 were considered) Figure 2.11 Wind electric potential of Sri Lanka based on districts (Areas 33 with a wind power density more than 400 Wm-2 were considered) Figure 2.12 Wind resource map for Sri Lanka developed by AWS 35 Truepower, LLC Figure 2.13 Wind resource atlas of Sri Lanka developed by Vortex 37 Figure 2.14 Average wind hazard map of Sri Lanka developed by DMC 39 Figure 3.1 Guidelines for measuring wind speeds using Beaufort scale 51 Figure 3.2 Wind and solar measuring stations in Sri Lanka 52 Figure 3.3 Index to 1:50000 maps of Sri Lanka 54 Figure 4.1 Application of Gringorten and Gumbel methods for 64 Bandarawela station

Figure 4.2 Application of Gringorten and Gumbel methods for 65 Nuwara Eliya station Figure 4.3 Application of Gringorten and Gumbel methods for 65 Hambantota station Figure 4.4 Basic wind speed contours for Sri Lanka in 3-second gust 67 wind speeds Figure 4.5 Basic wind speed contours for Sri Lanka in 10-minute 68 average wind speeds Figure 4.6 District based wind loading zones for Sri Lanka in 3-second 69 gust wind speeds Figure 4.7 District based wind loading zones for Sri Lanka in 10-minute 70 average wind speeds Figure 4.8 Proposed wind loading zones for Sri Lanka 72 Figure 5.1 Selected 1:50000 scaled survey maps and wind measuring 78 stations for the analysis Figure 5.2 Wind atlas development procedure in WAsP 79 Figure 5.3 Conversion of GPS coordinates to Kandawala coordinate 84 system using the World Coordinate Converter (online) Figure 5.4 Sample of coordinates defined map 84 Figure 5.5 Defining of surface roughness in WAsP 87 Figure 5.6 Digitized maps after defining the surface roughness values 88 Figure 5.7 Wind power distribution map generated by WAsP 89 Figure 5.8 Finalized wind power distribution map after increasing the 90 resolutions using MATLAB software Figure 5.9 Wind roses to illustrate annul distribution of wind for 91 Ponnalai and Pooneryn stations Figure 5.10 Comparison between the proposed and existing wind resource 92 maps for Sri Lanka Figure A.1 Instrument installation on Ponnalai station by SLSEA 103 Figure A.2 Instrument installation on Pooneryn station by SLSEA 104

Figure A.3 Instrument installation on Kokkilai station by SLSEA 105

Figure B.1 Application of Gringorten method for Anuradhapura station 106 Figure B.2 Application of Gumbel method for Anuradhapura station 106 Figure B.3 Application of Gringorten method for Badulla station 107 Figure B.4 Application of Gumbel method for Badulla station 107 Figure B.5 Application of Gringorten method for Bandarawela station 108 Figure B.6 Application of Gumbel method for Bandarawela station 108 Figure B.7 Application of Gringorten method for Batticaloa station 109 Figure B.8 Application of Gumbel method for Batticaloa station 109 Figure B.9 Application of Gringorten method for Colombo station 110 Figure B.10 Application of Gumbel method for Colombo station 110 Figure B.11 Application of Gringorten method for Deniyaya station 111 Figure B.12 Application of Gumbel method for Deniyaya station 111 Figure B.13 Application of Gringorten method for Galle station 112 Figure B.14 Application of Gumbel method for Galle station 112 Figure B.15 Application of Gringorten method for Hambantota station 113 Figure B.16 Application of Gumbel method for Hambantota station 113 Figure B.17 Application of Gringorten method for Jaffna station 114 Figure B.18 Application of Gumbel method for Jaffna station 114 Figure B.19 Application of Gringorten method for Katugastota station 115 Figure B.20 Application of Gumbel method for Katugastota station 115 Figure B.21 Application of Gringorten method for Katunayake station 116 Figure B.22 Application of Gumbel method for Katunayake station 116 Figure B.23 Application of Gringorten method for Kurunegala station 117 Figure B.24 Application of Gumbel method for Kurunegala station 117 Figure B.25 Application of Gringorten method for Maha Iluppallama station 118 Figure B.26 Application of Gumbel method for Maha Iluppallama station 118 Figure B.27 Application of Gringorten method for Mannar station 119 Figure B.28 Application of Gumbel method for Mannar station 119 Figure B.29 Application of Gringorten method for Monaragala station 120 Figure B.30 Application of Gumbel method for Monaragala station 120 Figure B.31 Application of Gringorten method for Nuwara Eliya station 121 Figure B.32 Application of Gumbel method for Nuwara Eliya station 121

Figure B.33 Application of Gringorten method for Polonnaruwa station 122 Figure B.34 Application of Gumbel method for Polonnaruwa station 122 Figure B.35 Application of Gringorten method for Pottuvil station 123 Figure B.36 Application of Gumbel method for Pottuvil station 123 Figure B.37 Application of Gringorten method for Puttalam station 124 Figure B.38 Application of Gumbel method for Puttalam station 124 Figure B.39 Application of Gringorten method for Rathmalana station 125 Figure B.40 Application of Gumbel method for Rathmalana station 125 Figure B.41 Application of Gringorten method for Ratnapura station 126 Figure B.42 Application of Gumbel method for Ratnapura station 126 Figure B.43 Application of Gringorten method for Tawalama station 127 Figure B.44 Application of Gumbel method for Tawalama station 127 Figure B.45 Application of Gringorten method for Trincomalee station 128 Figure B.46 Application of Gumbel method for Trincomalee station 128 Figure B.47 Application of Gringorten method for Vavuniya station 129 Figure B.48 Application of Gumbel method for Vavuniya station 129 Figure C.1 Wind roses for Ponnalai station for each month of the year 130 Figure C.1 Wind roses for Pooneryn station for each month of the year 131

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LIST OF TABLES

Page

Table 1.1 Basic wind speeds in different wind loading zones in Sri Lanka 3 Table 1.2 Different averaging time periods which have been used to 3 define the basic wind speed in several codes of practice for wind actions Table 2.1 Basic wind speed values proposed by Nandalal and Abeyruwan 14 Table 2.2 Reference wind speed values for Germany proposed by 21 Kasperski Table 2.3 Comparison between measured and theoretical values between 23

푈(3−푠푒푐) and 푈(푚푎푥) by Hsu at selected stations in USA Table 2.4 WMO recommendation for conversion between several 25 averaging time periods for tropical cyclonic conditions Table 2.5 Wind energy potential of several regions in Sri Lanka 40 Table 2.6 Roughness coefficients for different surface characteristics 45 Table 2.7 Summary of the wind resource maps developed for Sri Lanka 47 Table 3.1 A sample AWS data sheet for Anuradhapura weather station 48 Table 3.2 Available wind data of major weather stations in Department 49 of Meteorology, Sri Lanka Table 3.3 Available AWS wind data in Department of Meteorology, Sri 50 Lanka Table 3.4 Available types of data records in SLSEA 53 Table 4.1 Assigned ‘푝′’ Values for each AWS 58 Table 4.2 Assigned gust conversion factors for each weather station based 60 on WMO guidelines Table 4.3 Predicted extreme wind speeds using Gringorten method for each 61 weather station and for several return periods Table 4.4 Predicted extreme wind speeds using Gumbel method for each 62 weather station and for several return periods

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Table 4.5 RMSE2 values for each station for both Gringorten and Gumbel 63 Method Table 4.6 Proposed basic wind speeds for each wind loading zone 71 Table 5.1 Detailed comparison between the proposed and existing wind 94 resource maps for Sri Lanka

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LIST OF ABBREVIATIONS

Abbreviation Description

AWS Automatic Weather Station CEB Ceylon Electricity Board CFD Computational Fluid Dynamics DEM Digital Elevation Model DMC Disaster Management Center GEV Generalized Extreme Value LA Louisiana LKR Sri Lankan Rupees MS Mississippi NCAR National Center for Atmospheric Research NCEP National Centers for Environmental Prediction NERDC National Engineering Research and Development Center NREL National Renewable Energy Laboratory NWP Numerical Weather Prediction RMSE Root Mean Square Error ROB Rough Observation Books SLSEA Sri Lanka Sustainable Energy Authority TX Texas USD United States Dollars WAsP Wind Atlas Analysis and Application Program WMO World Meteorological Organization WPD Wind Power Density WRF Weather Research and Forecasting

LIST OF SYMBOLS

퐴 Scale parameter

퐴퐻 Average horizontal area of the object 푒 The exponential constant (2.718)

퐹푈(푈′) Cumulative probability distribution function of the maximum wind speeds over a defined period, for an example one year 𝑔 The acceleration of gravity (9.8 ms-2)

퐺푘(훼) 1/푘 times the incomplete gamma function of the two arguments 1/푘 and 훼푘 ℎ Height of the object 푘 Shape factor 푚 Rank 푛 Number of records in the averaging interval 푁 Size of the data set 푝 Exponent of the power law of wind profile 푃 Air pressure

푃0 Atmospheric pressure at the standard sea level (101 325 Pa), or the actual sea level adjusted pressure reading from a local airport 푝′ Probability of non-exceedance 푃(푢) Power curve th 푃푖 Power at the 𝑖 node th 푃푖+1 Power at the (𝑖 + 1) node 푃푟(푢) Probability density function of 푢 푅′ The specific gas constant of air (287 Jkg-1K-1) 푅 Return period 푦 Reduced variant 푆 Cross sectional area of the object facing the wind direction 푇 Air temperature in Kelvins 푢 Wind speed at hub height 푈 Unknown wind speed at height z above ground

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푈0 Known wind speed at a reference height 푧0 푈′ Wind speed

푈(1−푚푖푛) 1-Minute average wind speed

푈(3−푠푒푐) 3-Second gust wind speed

푈(푚푎푥) Instantaneous maximum wind speed

푈푒푥푡푟푒푚푒 Predicted extreme wind speed for a return period of 푅 th 푢푖 Wind speed at the 𝑖 node th 푢푖+1 Wind speed at the (𝑖 + 1) node

푣푖 Wind speed 푊푃퐷 Unknown wind power density at height z above ground

푊푃퐷0 Known wind power density at a reference height 푧0 푧 Site elevation above sea level 푧 Height of wind measurement

푧0 Roughness length

푍0 Aerodynamic roughness length 훼 Power law exponent (For well exposed areas with low surface Roughness a value of 0.143 can be used) 휌 Air density

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LIST OF APPENDICES

Appendix Description Page

Appendix – A Positioning of Weather Measuring Instruments in the 103 Wind Masts Appendix – B Graphs Generated by Applying the Gringorten and 106 Gumbel Methods for the Weather Stations in Sri Lanka Appendix – C Graphs Generated to Visualize the Monthly Variations 130 in Wind for Northern Region in Sri La

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1. INTRODUCTION

1.1 Background

1.1.1 Adequacy of existing wind loading map for Sri Lanka

Wind load is one of the major lateral loads acting on civil engineering structures. An accurate estimation of the wind load is necessary in the design of structures that need to be safe and economical.

It was more than 40 years ago that Sri Lanka last established a wind loading map. The map shown in Figure 1.1 could have been developed based on the damage pattern observed during the severe cyclone that struck Sri Lanka in the year 1978 and the Indian wind loading map of that period [1]. Hence, it has not been based on any statistical method or recorded wind data. As far as this map is concerned, there are few ambiguities. In general, two wind speed contours cannot intersect each other. However, in the Ampara region, wind speed contours have become discontinuous. As a result, Zone 3 meets Zone 1 without any interference of Zone 2. Therefore, the 3- second gust for normal structures in that region abruptly increases from 33 ms-1 to 49 ms-1 (see Table 1.1), which is an increment of almost 50%.

In addition, during the past forty years the climate patterns around Sri Lanka have been subjected to significant variations. It can be seen that the number of extreme wind events which affected Sri Lanka per annum has been increased during the past few years. One example is the cyclone ‘Mora’ which affected Sri Lanka in May of 2017. It affected more than 500 000 people in Southern and Sabaragamuwa provinces [3]. In the month of December of the same year cyclone ‘Ockhi’ affected Sri Lanka and more than 77 000 people in Southern province were affected [3]. Also the ‘Roanu’ and ‘Nada’ cyclones which affected Sri Lanka in the year 2016 were responsible for more than 210 deaths [3]. However, the Southern and Sabaragamuwa provinces are located in the less critical zone (Zone 1) in the existing wind loading map (see Figure

1.1). Therefore, the long term changes of wind patterns due to global and local climatic changes need to be considered while developing a new wind loading map.

Jaffna Wind Loading Zones Sri Lanka

Mannar

Vavuniya Zone Trincomalee N 1

Anuradhapura Zone

Puttalam 2 Polonnaruwa

Dambulla Batticaloa

Kurunegala Matale Kalmunai

Kandy Ampara

Colombo Zone 3

Galle Hambantota

•Boundaries as proposed by Sri Lanka Cyclone Regulations Committee

•Possible modifications to above boundaries

Figure 1.1: Existing wind loading zones for Sri Lanka [2]

Table 1.1: Basic wind speeds in different wind loading zones in Sri Lanka [2] Wind Loading 3-Second Gust Speed for 50-Year Return Period (ms-1) Zone Post Disaster Structures Normal Structures Zone 1 54 49 Zone 2 47 42 Zone 3 38 33

To estimate the actions due to wind on structures Sri Lankan engineers use different wind loading codes of practice as indicated in Table 1.2.

Table 1.2: Different averaging time periods which have been used to define the basic wind speed in several codes of practice for wind actions [4]-[7]

Code of Practice Published Institution Averaging Time Period used for Wind Actions for Define Gust Wind Speed CP 3: Chapter V-2 British Standards 3-Second gust Institution BS 6399-2 British Standards Hourly mean Institution EN 1991-1-4 European Committee for 10-Minute average Standardization AS/NZS 1170.2 Standards Australia 3-Second gust

Some of those codes such as BS 6399 and European standards are using averaging time periods other than the 3-second gust to define the basic wind speed. However, the existing map has not been addressed this issue. It indicates the basic wind speeds only as 3-second gusts. Therefore, the 3-second gust has to be converted to other averaging times by using gust conversion factors, but those conversion factors depend on the surface roughness and the terrain variations in that considered location. Therefore, the direct conversion of 3-second gust to other averaging times would not be accurate, because inside a given zone, the surface roughness and the terrain can be varied significantly. Therefore, this issue also has to be considered while developing a new wind loading map for Sri Lanka.

1.1.2 Adequacy of existing wind resource maps for Sri Lanka

The demand for the electricity is being increased annually. Therefore, it is essential to find some solutions for the energy crisis in Sri Lanka. Wind energy is one of the sustainable and renewable energy sources available and Sri Lanka is a country surrounded by the Indian Ocean which has a higher wind energy potential.

Even though Sri Lanka has some large scale coal and hydropower plants, the government has decided to move in to sustainable power generation methods such as wind and solar power generation. The major benefit of sustainable power generation methods is the source gives energy totally free. Also the operational cost, heat emission, wastage to be dumped, noise pollution and carbon foot print value are very less compared to coal and nuclear power generation. Major disadvantages are massive initial investment, smaller output per station and the unpredictable variations of annual climate patterns.

In Sri Lanka there are certain government bodies which are working in order to utilize wind energy such as SLSEA (Sri Lanka Sustainable Energy Authority), CEB (Ceylon Electricity Board) and Department of Meteorology, etc. According to those organizations, three major regions were identified in Sri Lanka as the regions that contain higher wind energy potentials. They are,

• North and North-Western coastal regions • Central highlands • Some regions in Sabaragamuwa and Uva province

These three regions are mainly determined by the two Asian monsoons called South- Western Monsoon and North-Eastern Monsoon. The South-Western Monsoon appears from May to September while the North-Eastern Monsoon appears from December to March. The South-Western Monsoon is the strongest monsoon and it

affects the entire Northern and North-Western coastal region and some parts of the central highlands.

The wind flows generated in central highlands have turbulent characteristics and within a small region the characteristics of wind can be varied due to the enormous terrain variations. Although the wind flows generated in approximately flat surfaces such as in Northern and North-Western coastal regions are more consistent compared to the hilly regions.

The first wind power plant in Sri Lanka was established in the year 1999 in Hambantota region. ‘Wind Force’, ‘SENOK’ and ‘LTL Holdings’ are some of the leading wind power plant developers in Sri Lanka at the present time. The government buys the electricity from those companies. Since the operational cost for wind power plants is comparatively less, the production cost for energy of 1 kWh is around 0.02 to 0.15 USD depending of the efficiency of the turbine and the availability of the continuous wind flow throughout the year [8].

The major drawbacks of wind power plants are the massive amount spent for the establishment of wind turbines, consistency and their small power output to the grid. The approximate cost for the establishment of single 600 kW wind power plant is around 600 000 USD [8]. Hence the locations must be identified very carefully and accurately for the placement of wind turbines. Unless the expected output will not be provided at the end.

There exist several wind power distribution maps for Sri Lanka. However, those maps are not being used by the wind power plant developers to recognize the key regions for the establishment of wind power plants due to the low resolutions of those maps. Hence they use more expensive and time consuming but a reliable method to locate these wind energy hotspots. Initially a suitable region to locate the wind farm should be identified using either onsite observations or by using an existing wind resource map for Sri Lanka. The other key parameters that needed to be considered are the availability of the transmission grid, the availability of land and the transportation of

long fans of turbines. Even though the hill country side of Sri Lanka has a higher wind potential, there is a difficulty to transport long fans along the zigzag roads. Therefore, the wind farms were still not established in most of the hilly regions despite their higher wind potential.

After selecting some region with a higher wind potential, generally a wind mast will be established to monitor the wind velocities over a period of one year approximately. Addition to the construction cost of this wind mast, there will be a maintenance cost for a surveillance period of one year. To analyze the wind data and to identify the wind energy hotspots, a dataset of at least one-year surveillance period is required, because the wind pattern variations throughout the year is not uniform.

For the analysis of wind data, the industry uses a commercially available CFD (Computational Fluid Dynamics) based software package called WAsP. Using that a wind power generation map can be generated and the key areas which have the highest wind potential can be identified. After that depending on the availability of the transmission grid, land and convenience of transportation, the most effective locations for the establishment of wind turbines will be decided. However, those micro scale level maps are not being shared or published anywhere due to the competition among the leading wind power plant developers.

Therefore, it is imperative to develop a wind power distribution map with a higher spatial resolution which covers most of the regions in Sri Lanka which have the highest wind energy potential. This can be useful for wind power plant developers to identify the optimum locations to establish the wind turbines without on-site observations.

As a summary it was recognized that the existing wind loading and wind power distribution maps are needed an update with an adequate spatial resolution.

1.2 Scope of the Study

The existing wind loading map for Sri Lanka has been developed based on the damage pattern observed after the severe cyclonic incident that struck the North-Eastern and Northern coast of Sri Lanka during the year 1978. It is strongly believed that this map has not been developed based on neither past observed wind data nor any statistical approach. There are few research attempts which have been carried out to demarcate new wind zones for Sri Lanka. However, any of these attempts are currently not being used. Moreover, the existing map refers 3-second gust speeds for 50-year return period which cannot be used with codes such as Euro Code 1 or BS 6399. Also an accurate estimation of wind loading is necessary for design of safe and economical structures.

Several previous studies have been identified that the Northern and Central provinces are the regions which have the highest wind energy potential in Sri Lanka. SLSEA possesses wind data records from three regions in Northern province for a period of one year. Those data can be collected from SLSEA free of charges. There are several other institutions who possess wind data records at potential regions in the country. However, they do not intend to release those data to the public due to the competition among the wind farm developers. Therefore, the Northern province is the only region where the wind data can be obtained free of charges.

WAsP (Wind Atlas Analysis and Application Program) is a commercially available software package which can be used to analyze the observed wind data. The extrapolation of wind data can be done up to a maximum range of 50 km if the terrain is flat and less than 20 km if the terrain is more complex.

Considering all of these facts, this study focuses on demarcating of wind loading zones for Sri Lanka and developing of a micro scale level wind power distribution map for a selected region in Sri Lanka. As a case study the Jaffna Peninsula region was selected.

1.3 Objectives

The main objectives of this research are to develop a wind loading map for Sri Lanka and develop a micro scale level wind power distribution map for a potential region in the country. These objectives are listed below in details,

• Development of a wind loading map for Sri Lanka using past wind data and a probability based statistical approach for several return periods (5, 10, 50, 100, 200, 500 and 1000 years) and for different averaging times (3-second gust, 10- minute average and hourly mean). • Development of a micro scale level wind power distribution map for a selected region (Jaffna Peninsula) in Sri Lanka and identify the suitable location for siting the wind turbines.

1.4 Outcomes

In this dissertation, the following outcomes were generated,

• A wind loading map for Sri Lanka for several return periods (5, 10, 50, 100, 200, 500 and 1000 years) and for different averaging times (3-second gust, 10-minute average and hourly mean), which can be used with several wind codes such as CP 3: Chapter V-2, AS/NZS 1170.2, EN 1991-1-4 and BS 6399-2. • A micro scale level wind power distribution map for Jaffna Peninsula region in Sri Lanka which can be used to identify the key regions where the wind energy potential is maximum. • Suitable locations for siting wind turbines in Northern region of Sri Lanka.

1.5 Arrangement of the Dissertation

The structure of the dissertation consists with six chapters.

Chapter one describes the background of the study including the adequacy of existing wind loading and wind resource maps for Sri Lanka, the scope of the study, objectives and outcomes.

Chapter two includes the literature review. This chapter initially describes the different standards which are used to determine the wind action on structures, several attempts taken to demarcate wind loading zones for several countries including Sri Lanka, some statistical methods that can be used to predict the extreme wind speeds and some related researches for gust conversions. It also describes the adequacy of existing wind resource maps for Sri Lanka, several approaches made towards wind energy forecasting, use of WAsP software package in wind power prediction and some important relationships in wind energy forecasting.

Chapter three refers to data collection. In this chapter the available types of wind data and topographical maps, their sources and characteristics are described.

Chapter four describes the methodology followed to develop the wind loading map for Sri Lanka and the generated results.

Chapter five describes the methodology followed to develop the wind power distribution map for Jaffna Peninsula in Sri Lanka and the generated results.

Chapter six contains the conclusion and recommendations of this study.

2. LITERATURE REVIEW

2.1 Different Standards Which are used to Calculate the Wind Actions on Structures

Sri Lankan engineers use different standards to calculate the actions due to wind on structures. These standards can be as old as the CP 3: Chapter V-2 or as new as AS/NZS 1170.2, BS 6399-2 or EN 1991-1-4. Different codes of practice use different averaging times to define the basic wind speed at a given location.

Basic wind speed can be defined as the gust speed which will be exceeded only once during a return period of 50 years, irrespective of its direction of wind, at a height of 10 m above a completely flat terrain at mean sea level. Here it is assumed that the roughness of the surface is uniform everywhere.

Codes of practice such as CP 3: Chapter V-2 and AS/NZS 1170.2 consider the basic wind speed in 3-second gust while BS 6399-2 and Euro Code 1 consider the basic wind speed in hourly mean and 10-minute average respectively [4]-[7]. However, the existing and proposed wind loading maps for Sri Lanka only indicate the basic wind speeds in 3-second gusts. Therefore, the 3-second gust has to be converted to other averaging times by using gust conversion factors, but those conversion factors depend on the surface roughness and the terrain variations in that considered location. Therefore, the direct conversion of 3-second gust to other averaging times would not be accurate, because inside a given zone, the surface roughness and the terrain can be varied significantly.

2.2 Attempts towards Wind Zone Demarcation for Several Countries

2.2.1 An attempt towards wind zone demarcation for Sri Lanka

An attempt was made by Nandalal and Abeyruwan [9] in 2010 to demarcate wind loading zones for Sri Lanka. Figure 2.1 shows the proposed wind loading map which is a completely different one from the existing map. The Zone 1 which is marked as the most critical zone in the existing map (see Figure 1.1 and Table 1.1) can be identified as the least critical zone in this proposed map (see Figure 2.1). This could be unacceptable for the engineering community since the north-eastern coast of the country is the most vulnerable region for cyclonic attacks due to the depressions occurred at the Bay of Bengal (see Figure 2.2). Hence, the proposed map by Nandalal and Abeyruwan had not been accepted and currently it is not being used.

In the map mentioned in Figure 2.1, the basic wind speed value difference from Zone 1 Zone 2 has become 30 ms-1 while it has become 15 ms-1 from Zone 2 to Zone 3 (see Table 2.1). Therefore, this can be considered as a map with very low resolution, since the basic wind speed can be varied suddenly by an amount of 30 ms-1 depending on the location, which is too much. In urban regions, such as along the coastal line of Western and Southern provinces, the calculated wind loads could be much higher as the basic wind speed of those regions are about 60 ms-1.

In the map mentioned in Figure 2.1 was based only on wind data recorded at 15 out of 23 weather stations in Sri Lanka. The considered data period for the analysis is about 10 years. At some stations such as Katugastota and Kurunegala, the data obtained have been recorded over a period of less than 5 years. In general, to predict the extreme wind speeds for a return period of 50 years, data of at least a period of half of that time (25 years) needs to be considered.

Furthermore, it was identified that some of the important wind data for the considered period (generally around 30 to 50 daily wind speeds per year and cyclonic data) was missing or was not considered during the analysis. Therefore, there is a possibility

that the actual extreme wind speed value of a particular year has been missed accidently.

Legend

Wind Speed (ms-1) 00-15 15-30 30-60

Zone 3 (00-15 ms-1)

Zone 2 (15-30 ms-1) Zone 1 (30-60 ms-1)

Figure 2.1: Wind loading zones proposed by Nandalal and Abeyruwan [9] (Original in Colour)

Figure 2.2: Tracks of past cyclones and storms through Sri Lanka [3] (Original in Colour)

Table 2.1: Basic wind speed values proposed by Nandalal and Abeyruwan [9] Wind Loading Zone 3-Second Gust Speed for 50-Year Return Period (ms-1) Zone 1 60 Zone 2 30 Zone 3 15

2.2.2 Maximum wind hazard map for Sri Lanka developed by DMC

Recently DMC (Disaster Management Center) of Sri Lanka has developed a map which shows the maximum wind speeds of Sri Lanka [3] (see Figure 2.3). To develop this map, the potential wind hazards due to cyclonic events formed in the Bay of Bengal and the cyclones that crossed or moved away within a 100 km distance from the coastal margin were considered. The wind data collected from NCEP (National Centers for Environmental Prediction) and NCAR (National Center for Atmospheric Research) were incorporated into WRF (Weather Research and Forecasting) model during the analysis. After executing the model, the highest wind speeds at each point over Sri Lanka was obtained to develop the maximum wind hazard map with a spatial resolution of 10 km x 10 km (0.090 x 0.090). The Kriging interpolation technique was used to interpolate the values between weather stations.

In this map the wind speed values were measured in Knots. Since the converted value of 1 Knot equals to 0.5144 ms-1, nowhere of the country in this map has been exceeded the wind speed value of 35 ms-1. Also the maximum wind speed in Colombo district of this map has become 18 ms-1. Those results are unacceptable, because the recent gust speeds recorded all over the country prove that those values have been exceeded in several years during the period of past 50 years.

The recent cyclonic events which affected Sri Lanka has proven that some regions in Sabaragamuwa province have been experienced higher wind speeds. Any of the existing or proposed wind loading maps have not been incorporated with higher wind speeds more than 33 ms-1 over this region.

Figure 2.3: Maximum wind hazard map of Sri Lanka developed by DMC [3] (Original in Colour)

Because of these undesirable aspects noted in both the existing and the proposed wind loading maps of Sri Lanka, an updated wind loading map has become a necessity.

2.2.3 Development of basic wind speed map for Oman

Basic wind speeds for Oman were determined by Alnuaimi et al. [10] using hourly mean wind speeds recorded over 40 weather stations. However, the determined wind speeds were presented only at individual stations rather than demarcating them either as basic wind speed contours or as wind loading zones (see Figure 2.4).

The period of obtained continuous records was ranged between 4 to 37 years. The locations which contained shorter period of wind data have not been considered for the analysis. The maximum annual hourly mean and the maximum monthly hourly mean wind speeds were analyzed using both Gringorten and Gumbel methods. Both methods have shown some closer results, but the Gumbel method has shown slightly higher values. Finally, the Gumbel method was adopted to predict the basic wind speeds for a 50-year return period.

Basic Wind Speeds for Sultanate of Oman

(All wind speeds are hourly mean wind speeds for a 50-year return period & measured in ms-1)

Figure 2.4: Basic wind speeds for Oman which was determined by Alnuaimi et al. [10]

2.2.4 Development of basic wind speed map for India

In more recently, Lakshmanan et al. [11] have updated the basic wind speed map for India using long-term wind speed records from 70 metrological stations. The daily gust wind data were processed to find the annual maximum wind speed for each station. Gumbel method was adopted to derive the extreme wind speeds for a 50-year return period. Finally, wind loading zones have been demarcated for India.

However, in the proposed map a clear discontinuity can be seen between some wind zones. As an example in the topmost region, the basic wind speed suddenly varies from the second lowest value (yellow coloured zone) to the highest value (maroon coloured zone) without any interference of the other three intermediate zones. Therefore, the 3-second gust wind speed in that particular region abruptly increases from 39 ms-1 to 55 ms-1, which is an increment of more than 40% (see Figure 2.5).

According to this map the basic wind speed for South-Eastern coastal region of India is indicated as 50 ms-1. The existing wind loading map for Sri Lanka indicates a similar wind speed for North-Eastern coast as 49 ms-1 (see Figure 1.1 and Table 1.1). It can be seen that both of these regions are exposed to the cyclonic events occurred in the Bay of Bengal.

Figure 2.5: Basic wind speed map for India suggested by Lakshmanan et al. [11] (Original in Colour)

2.2.5 Development of wind zone map for Germany

Kasperski [12] has revised the old basic wind speed map for Germany, which was developed after the year 1980. The data used were daily maximums of hourly mean wind speeds and their mean wind directions, the daily maximums of 2-3 second gust wind speeds and their respective times of occurrence. Altogether, the data over an average period of 40 years in 183 weather stations have been considered in this study. Individual maximums due to cyclonic events over a particular year were considered as separate data records to increase the size of the data set.

A type III extreme value distribution which is called as Weibull distribution was used to predict the extreme wind speeds. These type of distributions have shown more reliable results for countries such as Germany, Australia, etc. which are located closer to the North or South poles. Since the Eurocodes use 10-minute average wind speeds, all the results have been factorized using a factor 1.06 to convert them to 10-minute averages.

Finally, a new wind zone map was proposed which is shown in Figure 2.6. The country has been divided in to five wind zones in intervals of 2.5 ms-1, which follow mainly the boundaries of the administrative districts of Germany (see Table 2.2). Even though these are wind zones, unlike in the Indian map a continuous variation of basic wind speeds can be seen between the margins of wind zones. It was recommended to use this map to determine the actions due to wind accordance with Euro Codes.

Figure 2.6: Wind zone map for Germany proposed by Kasperski [12]

Table 2.2: Reference wind speed values for Germany proposed by Kasperski [12]

Wind Zone I II III IV V -1 vref (ms ) 22.5 25.0 27.5 30.0 32.5

2.3 Conversion between Gust Speeds for Different Averaging Periods

Figure 2.7 illustrates the basic difference between the instantaneous maximum and the 3-second gust recorded over a period of 1 month. As shown in Figure 2.7(a), instantaneous maximum is the highest instant wind speed recorded over a given time period. 3-Second gust is the highest of the averaged wind speeds recorded during intervals of 3 seconds over a given time period as shown in Figure 2.7(b).

Wind Speed Wind Speed

Instantaneous maximum

+ 3-Second _ gust

3 sec. Time Time

For example, For example, Period of 1 month Period of 1 month (a) (b)

Figure 2.7: Comparison between instantaneous maximum and 3-second gust

Hsu [13] developed equations that can be used to predict 3-second gusts and instantaneous maximum wind speeds using 1-minute average wind speeds as shown in Equations 1 and 2.

푈(3−푠푒푐) = 푈(1−푚푖푛). (1 + 2푝) (1)

푈(푚푎푥) = 푈(1−푚푖푛). (1 + 3푝) (2)

Also Hsu verified those equations using wind speeds obtained from 7 stations in USA. Those results are shown in Table 2.3

Table 2.3: Comparison between measured and theoretical values between 푈(3−푠푒푐)

and 푈(푚푎푥) by Hsu [13] at selected stations in USA Deployment Site Measured Theoretical Measured Theoretical 푼(ퟑ−풔풆풄) 푼(ퟑ−풔풆풄) 푼(풎풂풙) 푼(풎풂풙) (ms-1) from Eq. (1) (ms-1) from Eq. (2) (ms-1) (ms-1) Vacherie, LA 31.4 32.1 32.8 35.8 Slidell, LA 38.5 40.3 44.6 45.0 Bay St. Louis, MS 47.1 39.1 52.3 43.6 Anahuac , TX 37.9 36.1 40.5 40.3 Winnie, TX 38.6 40.8 40.7 45.5 Port Arthur, TX 51.9 54.3 53.8 60.6 Orange, TX 39.3 38.0 42.1 42.3 Mean 40.7 40.1 43.8 44.7

Exponent of the power law of wind profile (푝) is a function of the roughness of the surface. Panofsky and Dutton [14] suggested Equation 3 to determine the value of 푝 for a selected terrain when the surface roughness value of the terrain is known.

1 푝 = (3) 푍 푙푛 ( ) 푍표

Values for 푍표 depend on the nature of the ground surface as shown in Figure 2.8 [15].

Figure 2.8: Roughness lengths (푍표) for different types of surfaces [15]

The wind speeds obtained by several meteorological stations may be recorded from different averaging periods depending on the available type of anemometers and available number of staff members to take the onsite measurements. WMO (World Meteorological Organization) has provided guidelines to convert wind speeds between several averaging time periods over a tropical cyclonic condition [16], as indicated in Table 2.4.

Table 2.4: WMO recommendation for conversion between several averaging time periods for tropical cyclonic conditions [16]

Exposure at +10 m Reference Gust Conversion Factor from T0 to τ Class Description Period Gust Duration (τ) (T0) 3 Sec 1 Min 2 Min 3 Min 10 Min 3 Sec 1.00 0.67 0.65 0.63 0.60 1 Min 1.49 1.00 0.88 0.87 0.82 Roughly 2 Min 1.55 1.13 1.00 0.93 0.89 In-land open terrain 3 Min 1.58 1.15 1.07 1.00 0.92 10 Min 1.66 1.21 1.12 1.09 1.00 1 Hour 1.75 1.28 1.19 1.15 1.08 3 Sec 1.00 0.74 0.70 0.69 0.66 1 Min 1.36 1.00 0.93 0.91 0.86 Offshore 2 Min 1.42 1.08 1.00 0.96 0.92 Off-land winds at a 3 Min 1.44 1.10 1.04 1.00 0.94 coastline 10 Min 1.52 1.16 1.09 1.06 1.00 1 Hour 1.60 1.22 1.15 1.12 1.06 3 Sec 1.00 0.81 0.78 0.76 0.72 1 Min 1.23 1.00 0.97 0.95 0.90 Onshore 2 Min 1.28 1.03 1.00 1.00 0.95 Off-sea winds at a 3 Min 1.31 1.05 1.00 1.00 0.97 coastline 10 Min 1.38 1.11 1.05 1.03 1.00 1 Hour 1.45 1.17 1.11 1.09 1.05 3 Sec 1.00 0.90 0.87 0.85 0.81 1 Min 1.11 1.00 1.00 1.00 0.95 >20 km 2 Min 1.15 1.00 1.00 1.00 0.98 At-sea offshore 3 Min 1.17 1.00 1.00 1.00 1.00 10 Min 1.23 1.05 1.02 1.00 1.00 1 Hour 1.30 1.17 1.07 1.06 1.03

2.4 Probabilistic Methods of Predicting Extreme Wind Speeds

Extreme value distributions are statistical method that deals with the extreme events. There are three types of extreme value distributions which can be defined as Type I, II and III. They can be useful to model the maximum or minimum values for a collection of random data set. The famous Gumbel and Gringorten distributions are Type I distributions while Fréchet and Weibull distributions belong to Type II and Type III respectively. Another distribution called GEV (Generalized Extreme Value) distribution was developed by A. F. Jenkinson [17] in 1955 by combined above three GEV distributions. All of above distributions are useful to predict the extreme wind speeds in particular location for a given return period.

Type I extreme value distributions such as Gringorten and Gumbel are more suitable for fitting data records related to natural events such as floods, earthquakes and wind [18].

However, there are some common limitation for the extreme value distributions. One limitation is the values should not be discrete, but most of the recorded wind data are discrete values which can be repeatedly occurred. Theoretically a continuous (non- discrete) data cannot be repeatedly occurred ever. Another limitation is the data should be unbiased and stationary. However, if some wind measuring stations use different techniques or different anemometer types time to time, the data recorded could be biased.

2.4.1 Gumbel distribution

Gumbel is a Type I extreme value distribution which was found by E. J. Gumbel in 1955 [19]. In most of the countries the Gumbel method has been adopted to predict the extreme wind speeds.

As the initial step of the Gumbel method, the data set which contains 푁 number of annual maximum wind speeds (푈′) is sorted in the ascending order and ranked from 1 to 푁. In the other words, the Rank (푚) is defined such that 푚 = 1, 2, 3 … … 푁, where 푁 is the size of the data set. Then probability of non-exceedance (푝′) can be calculated using

Equation 4. Then the reduced variant (푦) can be calculated using Equation 5.

푚 푝′ ≅ (4) 푁 + 1 푚 푦 = − ln [−푙푛 ( )] = − ln[−푙푛(푝′)] (5) 푁 + 1

Then a graph can be plotted for 푈 versus 푦 to illustrate the best fit straight line using linear regression (least squares method) as shown in Equation 6. Finally, the extreme wind speed for a given return period (R) can be calculated using Equation 7.

푈′ = 푢 + 푎푦 = 푢 + 푎{−푙푛[−푙푛(푝′)]} (6) 1 푈′ = 푢 + 푎 {− ln [−푙푛 (1 − )]} (7) 푒푥푡푟푒푚푒 푅

Where, 푢 = Mode (푈′ intercept when 푦 equals to 0) 푎 = Slope (Gradient of the best fit straight line)

2.4.2 Gringorten distribution

Gringorten distribution is very similar to Gumbel method which was developed by I. Gringorten in 1963 [20]. Compared to the Gumbel method this method is unbiased to determine the extreme values for a randomly distributed positive data set related to natural events such as flood, earthquake, wind, etc. Gumbel curve is biased from its top and bottom ends where is most critical region while determining the extreme values [18]. The only difference between Gumbel and Gringorten methods is the way of finding the probability of non-exceedance (푝′). The probability of non-exceedance need to be found using Equation 8. All of the other steps are similar to the Gumbel methods.

푚 − 0.44 푝′ ≅ (8) 푁 + 0.12

2.4.3 Generalized extreme value distribution

GEV was developed by A. F. Jenkinson [17] in 1955 by combining three types of extreme value distribution functions into a single mathematic form as shown in Equation 9. 1 푘(푈′ − 푢) 푘 퐹 (푈′) = exp {− [1 − ] } (9) 푈 푎

Where, 푎 = A scale factor 푢 = A location parameter

When 푘 < 0, Equation 9 becomes a Type II extreme value distribution (Fréchet distribution). When 푘 > 0, it becomes a Type III extreme value distribution (a form of the Weibull distribution). When 푘 = 0, the limits of Equation 9 reaches to generate a Type I extreme value distribution (Gumbel distribution) as shown in Equation 10 and 11.

(푈′ − 푢) 퐹 (푈′) = exp {− exp [− ]} (10) 푈 푎 (푈′ − 푢) = − ln{− ln[퐹 (푈′)]} (11) 푎 푈

When the curve of (푈′ − 푢)/푎 vs. − ln{− ln[퐹푈(푈′)]} is plotted, it can be observed a straight line only in Type I extreme value distributions. That is why the Type I extreme value distributions such as Gumbel and Gringorten methods are easily usable methodologies for fitting recorded annual maxima to predict their extreme values [18].

2.5 Attempts towards Demarcating Wind Energy Resource Atlases for Sri Lanka

2.5.1 Wind energy resource atlas of Sri Lanka and the Maldives developed by NREL

In the year 2003 NREL (National Renewable Energy Laboratory) of US Department of Energy and US Agency for International Development together have developed a wind energy resource map for Sri Lanka and Maldives (see Figure 2.9) [21]. This map has been developed mainly based on the satellite data taken from US military satellites, meteorological data and a DEM (Digital Elevation Model) of Sri Lanka. The meteorological data taken from 14 weather stations in Sri Lanka were used for the analysis. Wind power densities at a height of 50 m from the ground level were calculated with a spatial resolution of 400 m x 400 m. It has been more than 15 years ago that this map was developed. Therefore, the variation of wind patterns and surface roughness changes due to the development in land usage may affect the accuracy of the map.

Currently this is the accepted wind power distribution map for Sri Lanka, even though the wind plant developers do not use this map for decision making during the placement of wind turbines, because the reliability is not guaranteed since the map was in macro scale level. Also the authors have made a statement in the report such that the developers of this map do not take the legal liability or responsibility regarding the accuracy or the completeness of the information included in this report.

The total windy areas with moderate to excellent wind potential (areas which have a wind power density more than 300 Wm-2 including the lagoons) were estimated to be more than 11 000 km2, or about 15% of the total land area of the country [21]. This much amount of windy area could tolerate more than 51 000 MW of installed capacity. During the calculation it was assumed that the installed capacity per square kilometer as 5 MW and the total land area of the country as 65 600 km2.

Figure 2.9: Wind energy resource map of Sri Lanka developed by NREL [21] (Original in Colour)

Figure 2.10 indicates the provinces which have moderate to excellent wind potential in Sri Lanka [21].

Figure 2.10: Wind electric potential of Sri Lanka based on districts (Areas with a wind power density more than 300 Wm-2 were considered) [21] (Original in Colour)

While developing this map only the windy land areas were considered by excluding reserves, national parks, cultural and archeological sites by considering the details provided by the Urban Development Authority of Sri Lanka.

Similarly, the total windy regions with good to excellent wind energy potential (which have a wind power density more than 400 Wm-2 including the lagoons) were estimated to be nearly 5000 km2, or about 6% from the total area of the land of the country [21]. This much amount of windy regions could support more than 20 000 MW of installed capacity. Figure 2.11 indicates the provinces which have good to excellent wind energy potential in Sri Lanka [21].

Figure 2.11: Wind electric potential of Sri Lanka based on districts (Areas with a wind power density more than 400 Wm-2 were considered) [21] (Original in Colour)

According to Figures 2.10 and 2.11 the Northern province is one of the highest wind electric potential regions in Sri Lanka.

2.5.2 Wind resource atlas of Sri Lanka developed by AWS Truepower, LLC

A wind resource atlas for Sri Lanka was developed by a US agency called AWS Truepower, LLC [22]. AWS Truepower, LLC is a world leader in the science and application of resource mapping and modeling. The map provides mean annual wind speeds at a height of 80 m with a spatial resolution of 200 m x 200 m (see Figure 2.12).

This map was developed based on advanced atmospheric data and historical weather data. To develop this map a Meso Map model was used which was a combination of two atmospheric models. Those two different models were a mesoscale numerical weather prediction model and a microscale wind flow model.

A mean annual wind speeds map and a wind power distribution map are not the same thing. The reason for this is because the wind power density (mean wind power over a unit area) is proportional to the third order of the wind speed (see Equation 12). The map developed by NREL [21] is a wind power distribution map. Even though the map developed by AWS Truepower has a higher spatial resolution compared the map developed by NREL, it is not accurate to use a mean annual wind speed map to locate the areas with highest wind energy potential. In addition, the developers have indicated that this map is provided as a general indication of the wind resource and it is not intended for project design. Because of those reasons this wind atlas also is not being in use to locate the wind energy hotspots.

푛 1 Wind power density = ∑(휌푣 3) (12) 2푛 푖 푖=1

Figure 2.12: Wind resource map for Sri Lanka developed by AWS Truepower, LLC [22] (Original in Colour)

2.5.3 Wind resource atlas of Sri Lanka developed by Vortex

A wind resource atlas for Sri Lanka was developed by a Spanish private company called Vortex in 2014 [23]. This map shows mean annual wind speeds at a height of 100 m with a spatial resolution of 3 km x 3 km (see Figure 2.13). The spatial resolution is not adequate compared to the maps discussed in Sections 2.5.1 and 2.5.2. Therefore, it cannot be used for micro citing of wind turbines.

A WRF Model was used to develop this atlas. It is a macro scale NWP (Numerical Weather Prediction) system designed for both atmospheric research and operational forecasting requirements. Since the colours that have been used to represent the wind speed values are continuously varied (not discrete), there is a difficult to read the wind speed value for a particular location based on their coordinates.

Figure 2.13: Wind resource atlas of Sri Lanka developed by Vortex [23] (Original in Colour)

2.5.4 Average wind hazard map of Sri Lanka developed by DMC

Recently DMC of Sri Lanka has developed a map which shows the average wind speeds of Sri Lanka [3] (see Figure 2.14). To develop this map, the wind data collected from NCEP and NCAR were incorporated into WRF model. After executing the model, the average wind speed for each point in the country was obtained to prepare the map with a spatial resolution of 10 km x 10 km (0.090 x 0.090). The Kriging interpolation technic was used to interpolate the values between weather stations. This map is having a very low spatial resolution compared to the other maps in previous sections. Therefore, this cannot be used for micro siting of wind turbines.

Figure 2.14: Average wind hazard map of Sri Lanka developed by DMC [3] (Original in Colour)

2.5.5 Solar and wind resource assessment in Sri Lanka by NERDC

NERDC (National Engineering Research & Development Center) of Sri Lanka has conducted this assessment to assess the solar and wind energy potential in Sri Lanka [24]. In this assessment any wind resource atlas for Sri Lanka has not been developed. Instead of that it has identified the key regions which have the highest wind energy potential by referring ‘Wind energy resource atlas of Sri Lanka & Maldives’. Those regions are shown in Table 2.5.

Table 2.5: Wind energy potential of several regions in Sri Lanka [24] No. Region Wind Resource (Wm-2) 1 Central hills Above 800 2 Surrounding coastal hills 500-800 3 Islands surrounding Jaffna Peninsular 500-600 4 Northern and North-Western coastal area 400-500 5 Northern and North-Western regions away from the coastal 300-400 area 6 South-Eastern valley 300-400 7 South-Eastern region far from the coastal area 200-300 8 Northern region away from the coastal area (towards North- 200-300 Central region), North-Central and Southern region (away from the central hills)

2.6 WAsP (Wind Atlas Analysis and Application Program)

WAsP is a commercially available software package which can be useful for generating wind atlases, wind data analysis, wind climate estimation, wind farm power production calculations and siting of wind turbines. It is currently being used by the industry to site the wind farms and turbines. This software package was created and developed by Risø National Laboratory in Technical University of Denmark [25].

In WAsP software, the measured wind data of at least a period of one-year should be considered to get reliable results, because the wind pattern varies throughout the year.

The wind data observed from a single or several points can be extrapolated to the surrounded region up to a distance of 50 km maximum if the terrain is flat. If it is a hilly terrain such as central highlands, this range should be less than 20 km depending on the terrain to obtain reliable results. This program can extrapolate the wind data in both horizontal and vertical directions. It will consider the effects of the sheltering obstacles, surface roughness as well as the terrain height variations in to account during the calculation. After the analysis part this program has the ability to present the results as a wind resource map.

Conceptually, WAsP consists with five main calculation blocks [25]:

 Wind climate estimation  Analysis of raw wind data  Preparation of wind atlases  Calculation of wind farm production  Estimating the wind power potential

2.7 Several Approaches Made towards Wind Energy Forecasting

A mesoscale meteorological model and a CFD model was used to downscale the NWP data by Al-deen et al. [26] in 2006. The results obtained by both methods were verified with the data collected from Hachĳo Jima wind power plant in Japan. Coefficient matrix method was used to reduce the computational cost of the mesoscale model.

The terrain in Japan is generally very complex and it was identified that at least 10 m x 10 m spatial resolution is required to accurately take the effect of local terrain into account. Downscaling of the local wind up to a 10 m x 10 m spatial resolution has been carried out by a CFD model.

In the mesoscale model the RMSE (Root Mean Square Error) has been reduced to 3.1 ms-1 and in the CFD model it has been reduced to 2.8 ms-1 while in the original NWP it

was 5.7 ms-1. The coefficient matrix method has reduced the computational time from two hours using parallel personal computers with 8 CPUs to a few seconds with a single personal computer. However, it has not increased the prediction error.

A study was carried out regarding the mesoscale modeling which can be useful as tool for wind resource mapping and assessments by Brower et al. in 2004 [27]. The mesoscale modeling has been used widely in wind resource assessments. However, it has mentioned that it will not be a substitution for on-site measurements but can be used to identify preliminary areas for wind farms.

In the process of validating these wind maps generated by the MesoMap system, few modeling challenges have been encountered. One of them was defining the surface roughness characteristics. It has been addressed by on-site verifications and using better sources for land cover data. The other challenges were defining the stable boundary layer and reducing the mesoscale grid resolution. Those issues were addressed by continuing the expansion of TrueWind’s distributed computer capability and by employing the mesoscale model in a non-hydrostatic mode, respectively. However, to increase the mesoscale resolution below than 500 m - 1000 m it requires a new mesoscale model which does not use a terrain-conforming grid.

More recently, a study was carried out to determine the wind power density in highly complex terrains using a CFD model by Dhunny et al. in 2017 [28]. It has been found that accurate CFD simulations are essential to locate wind farms. In this research it has been validated the mean wind power estimated by the CFD software WindSim using on- site measurements from data obtained at 9 weather stations at several heights located around a highly complex terrain.

It has been identified that the numerical solutions are very sensitive to the large amount of computational parameters that need to be considered by the operator. It has been investigated those computational parameters in details. The best model has been selected to investigate the identified hotspots where the wind farming is possible in Mauritius with considering the topographical requirements and land use. Finally, several wind

maps have been generated at four levels which are located on general heights of commercially used wind turbines. These maps could be used for decision making while locating the wind farms.

2.8 Some Important Relationships in Wind Engineering

Wind power density is the average wind power over a unit area which was described in Equation 12. This equation should only be used for individually data records obtained over averaging periods less than or equal to one hour. It should not be used with data records obtained over monthly or yearly averaging periods, because the wind power density is proportional to the third order of the wind speed.

Air density can be varied slightly due to the variations in atmospheric temperature and pressure as shown in Equation 13.

푃 휌 = (13) 푅′푇

If the atmospheric pressure is unknown Equation 14 can be used to determine the air density. Equation 15 can be generated by substituting the numerical constants for Equation 14.

푔푧 푃0 (− ) 휌 = ( ) 푒 푅′푇 (14) 푅′푇

푧 353.05 (−0.034 ) 휌 = ( ) 휀 푇 (15) 푇

Wind shear profile is a description of the change in wind speed with the height variations. The Equation 16 can be used to extrapolate the wind speeds in the vertical direction. To extrapolate the WPD (Wind Power Density) in the vertical direction Equation 17 can be used

푧 훼 푈 = 푈0 ( ) (16) 푧0 푧 3훼 푊푃퐷 = 푊푃퐷0 ( ) (17) 푧0

The source for Equations 12 to 17 is [21].

푧0 is the height where the average wind speed equals to zero, by assuming that the wind profile has a logarithmic variation with height. This definition is valid generally for strong and moderate wind conditions. A simplified empirical relationship between the roughness length and the element has been developed by H. Lettau and K. Lettau [29] in 1969 as indicated in Equation 18.

ℎ. 푆 푍0 = 0.5 (18) 퐴퐻

A roughness element was characterized by its cross sectional area facing the wind and the height. Furthermore, for number of roughness elements which are scattered evenly, the density can be defined by the mean horizontal area of each element.

This relationship gave a reasonable estimation of 푍0 when 퐴퐻 is much higher than 푆.

It tends to overestimate 푍0 while 퐴퐻 is of the order 푆. This happens when the roughness elements are closer to each other, the wind will be lifted and pass over them. Then only a fraction of 푆 and ℎ contributes to the roughness. Furthermore, this flow lifting requires measuring the height above ground from somewhere else between the top and half of the height of the elements, which is called as the displacement length. The displacement length should often be taken into account in sites with forests, cities and tall vegetation. Finally, the equation assumes that the porosity is approximately zero. For porous roughness elements, 푍0 must be reduced by a fraction equal to the porosity.

Table 2.6 indicates the roughness coefficients values given in the European Wind Atlas which can be used with several surface characteristics.

Table 2.6: Roughness coefficients for different surface characteristics

Roughness Coefficient Surface Characteristics (m) Cities 1.0 Forests 0.8 Coconut cultivations 0.5 Suburbs 0.5 Shelter belts 0.3 Many trees 0.2 Bushes 0.2 Paddy cultivations 0.1 Farmlands with closed appearance 0.1 Farmlands with open appearance 0.05 Farmlands with very few buildings or trees 0.03 Airport runways 0.01 Bare soil 0.005 Mown grass 0.0008 Sand surfaces 0.0003 Water bodies 0.0002

2.9 Summary of the Chapter

The existing wind loading map for Sri Lanka has been developed more than 40 years ago. It was developed based on the damage pattern observed after a severe cyclone that struck the country in the year 1978. The present wind loading map of India at that period was also being used to predict the basic wind speeds for each zone. It is strongly believed that any statistical method had not been used in developing this map. This map contains several ambiguities such as discontinuity in wind speeds at some regions. Also the cyclone data in the recent past showed that the Sabaragamuwa and Southern provinces are also vulnerable regions for cyclonic hazards which can be originated at the Bay of Bengal. In addition, these climate patterns may also have been subjected

to variations since it has already been passed more than four decades. Hence, the map can either overestimate or underestimate the wind speeds at least in some of the regions of the country.

The existing wind loading map consists with 3-second gust wind speeds which can only be used with several codes for wind actions such as CP 3: Chapter V-2 and AS/NZS 1170.2. It cannot be used with codes such as BS 6399-2 and Euro Code 1, because those codes refer different averaging time periods. In addition, the existing map shows the basic wind speeds for a 50-year return period.

There were few attempts towards wind loading zone demarcation for Sri Lanka, but none of them have addressed above issues clearly. Generated maps from those attempts showed some unrealistic results. Therefore, Sri Lankan engineers still use the wind loading map developed in the year 1978.

Attempts for demarcation of basic wind speed maps for several countries were discussed. Gringorten and Gumbel methods were commonly used methods for extreme wind speed prediction in tropical climates. Out of those two the Gringorten method is more fitting and unbiased method than the Gumbel method.

WMO guidelines can be used to convert wind speeds between different averaging time periods. However, those guidelines did not provide any conversion factor between instantaneous maximum and the other averaging time periods. Therefore, for this conversion fundamental equations can be used which includes the surface roughness characteristics of the ground. To determine the surface roughness of the ground there some guidelines are available in the literature.

There are basically three types of extreme value distributions which are called as type I, II and III. The Gringorten and Gumbel methods are type I extreme value distributions which follow almost the same methodology. The only difference between those methods is that the Gringorten method has been included with some correction to maintain the linear nature at the top and bottom ends of the Gumbel curve,

because the top end of the curve is the most critical part while predicting the extreme wind speeds.

There are several wind resource maps available for Sri Lanka, but all of them have been developed in macro scale level with low resolutions which is not adequate for effective decision making in wind power generation. Moreover, most of them represents wind speed distributions other than the wind power distributions which are not the same. A summary of the wind resource maps available for Sri Lanka is shown in Table 2.7. Therefore, the industry always uses expensive methods to identify the key regions which are suitable for locating wind turbines.

Table 2.7: Summary of the wind resource maps developed for Sri Lanka Map Developer Resolution Hub Parameter Mentioned Units No. Height (a) NREL 400 m x 50 m Wind power density & Wm-2 400 m Mean annual wind speed ms-1 (b) AWS 200 m x 80 m Mean annual wind speed ms-1 Truepower, LLC 200 m (c) VORTEX 3000 m x 100 m Mean annual wind speed ms-1 3000 m (d) DMC 10000 m x Not Mean annual wind speed Knots 10000 m given

Few researches have been identified the Northern Province of Sri Lanka as one of the key regions which has a higher wind energy potential. Similarly, the central hilly region of the country also has a higher wind potential, but there are practical difficulties to establish wind power plants in these regions such as difficulty of transporting long wind fans of the turbine along the zig zag roads and the unavailability of power grid.

WAsP is a commercially available software package which can be useful for wind data analysis and for siting of wind turbines. Currently the industry uses this software as a tool to identify the key regions to locate wind turbines.

Finally, some important relationships in wind engineering was discussed.

3 DATA COLLECTION

3.1 Wind Data Available in the Department of Meteorology, Sri Lanka

The Department of Meteorology, Sri Lanka has been recording wind speeds and directions at a height of 10 m above ground level at 23 major weather stations in Sri Lanka. All of these weather stations have been located almost on flat terrains. They manually measure and manually record wind data in ROB (Rough Observation Books) as 3-minute averages once in every 3 hours (5 to 8 times per day depending on the availability of the operators for take night time observations).

Since the year 2009, AWS (Automatic Weather Stations) were introduced to measure the wind speeds. These stations have a higher accuracy of their readings and the ability to operate continuously. The continuously measured instantaneous wind speeds can be used to obtain the instantaneous maximum wind speed of a particular month. A sample data sheet for AWS wind data is shown in Table 3.1.

Table 3.1: A sample AWS data sheet for Anuradhapura weather station Station: Anuradhapura Coordinates: 08°20’ N, 80°23’ E Month Average Average Maximum Maximum Recorded Date and and Year Wind Wind Instantaneous Instantaneous Time of Maximum Speed Direction Wind Speed Wind Direction Instantaneous Wind (ms-1) (°) (ms-1) (°) Speed Oct-2009 1.8 163 21.9 244 10/07/2009, 09:15 Nov-2009 1.0 126 11.4 047 11/21/2009, 17:44 Dec-2009 1.2 110 18.3 083 12/19/2009, 13:12 Jan-2010 1.4 073 13.8 343 2010/01/22, 11:40 Feb-2010 1.3 071 9.6 300 2010/02/09, 14:43 Mar-2010 1.2 127 9.3 029 2010/03/22, 19:28 Apr-2010 1.2 186 13.2 113 2010/04/16, 17:37 May-2010 2.1 211 14.9 317 2010/05/27, 05:59 Jun-2010 2.7 227 14.7 270 2010/06/24, 15:24 Jul-2010 2.5 219 14.5 270 2010/07/01, 12:19 Aug-2010 2.5 223 13.3 252 2010/08/30, 14:15

Some of the details in the major and automatic weather stations in Sri Lanka are provided in Tables 3.2 and 3.3, respectively.

Table 3.2: Available wind data of major weather stations in Department of Meteorology, Sri Lanka Major Weather Period of Data Availability Coordinates of Station Available in Months the Station Data Records Longitude Latitude (o) (o) Anuradhapura 1978-2015 394 80.415 8.334 Bandarawela 1980-2015 425 80.985 6.839 Batticaloa 1976-2015 453 81.681 7.705 Colombo 1976-2016 455 79.872 6.905 Galle 1981-2009 197 80.214 6.030 Hambantota 1981-2014 388 81.129 6.122 Jaffna 1976-2015 236 80.033 9.694 Katunayake 1988-2015 334 79.878 7.167 Maha Iluppallama 1978-2015 357 80.468 8.111 Mannar 1978-2015 395 79.908 8.987 Nuwara Eliya 1980-2015 429 80.779 6.970 Pottuvil 1983-2015 308 81.833 6.883 Puttalam 1981-2015 405 79.842 8.027 Rathmalana 1981-1985 056 79.887 6.819 Ratnapura 1993-2015 247 80.383 6.717 Trincomalee 1976-2015 327 81.242 8.576 Vavuniya 1996-2015 223 80.502 8.745

Table 3.3: Available AWS wind data in Department of Meteorology, Sri Lanka

Automatic Period of Data Availability Coordinates of Weather Station Available in Months the Station Data Records Longitude Latitude (o) (o) Anuradhapura 2009-2015 63 80.415 8.334 Badulla 2009-2015 62 81.049 6.983 Bandarawela 2009-2015 50 80.985 6.839 Batticaloa 2009-2011 13 81.681 7.705 Colombo 2009-2016 84 79.872 6.905 Deniyaya 2009-2015 56 80.550 6.317 Hambantota 2009-2015 45 81.129 6.122 Katugastota 2009-2015 69 80.627 7.334 Katunayake 2009-2015 35 79.878 7.167 Kurunegala 2009-2015 64 80.353 7.480 Maha Iluppallama 2009-2015 52 80.468 8.111 Mannar 2011-2015 20 79.908 8.987 Monaragala 2009-2015 51 81.316 6.835 Nuwara Eliya 2009-2015 63 80.779 6.970 Polonnaruwa 2009-2012 21 81.050 7.913 Pottuvil 2011-2015 33 81.833 6.883 Puttalam 2009-2015 63 79.842 8.027 Rathmalana 2009-2015 59 79.887 6.819 Ratnapura 2009-2014 39 80.383 6.717 Tawalama 2009-2014 42 80.342 6.336 Vavuniya 2009-2015 31 80.502 8.745

In cases such as anemometer breakdowns, the Beaufort scale has been used to predict the wind speeds. It was developed by Sir Francis Beaufort in UK Royal Navy in 1805 (see Figure 3.1). Since it is a visual measurement of the wind speed, the values obtained depend on the observer’s perspective. Therefore, in this study such data have not been considered.

The cost of monthly maximum wind speed data set over a period of one year (12 readings) per station is 200 LKR. However, the Department of Meteorology has

provided all the wind data required for this study free of charges by considering the national importance of the output of this study.

Figure 3.1: Guidelines for measuring wind speeds using Beaufort scale

3.2 Wind Data Available in SLSEA

SLSEA measures and possesses climatic data related to wind and solar energy production in potential regions in Sri Lanka as shown in Figure 3.2. They record 10- minute average wind data continuously in potential regions for wind power generation. However, most of those records are not freely available for the public. The price of a wind data set over a period of one year per station is around 250 000 LKR excluding government taxes. However, 10-minute average wind data records for a period of one year (23.02.2015 to 23.02.2016) for three regions in Northern province are freely available at SLSEA. Those stations are mentioned below.

 Ponnalai, Jaffna District (9.74071 N, 79.92214 E)  Pooneryn, Kilinochchi District (9.57508 N, 80.09848 E)  Kokkilai, Mullaitivu District (9.57508 N, 80.09848 E)

Figure 3.2: Wind and solar measuring stations in Sri Lanka [30] (Original in Colour)

SLSEA has installed the anemometers in different heights in their wind measuring masts. Generally, instrument sets containing two anemometers and one wind vane have been installed at several altitudes of the wind mast. The anemometers located at the same altitude level are positioned opposite to each other in plan. This can minimize the error arising due to the disturbance coming from the mast during the wind measurements. It is also useful in emergency cases such as anemometer breakdowns. The data are recorded automatically at each 10-minute interval. Table 3.4 gives an idea regarding the data records available in SLSEA. All of those readings are having a minimum reading of 0.01. The position of installment of each instrument along the wind mast is described in the diagrams in Appendix – A. The average and standard deviation values for wind direction are generated by considering the vector average.

Table 3.4: Available types of data records in SLSEA Instrument and the Position Measured Unit Measured Categories Number of Amount from the Parameter at each Avg. St. D. Max. Min. Placed Top of 10-Minute the Mast Interval Anemometer (2) Level 1 Wind speed ms-1 √ √ √ √ Thermistor (1) Level 1 Air temperature 0C √ √ √ √ Wind vane (1) Level 2 Wind direction 0 √ √ - - Anemometer (2) Level 3 Wind speed ms-1 √ √ √ √ Wind vane (1) Level 4 Wind direction 0 √ √ - - Anemometer (2) Level 5 Wind speed ms-1 √ √ √ √ Humidity sensor (1) Level 6 Relative humidity % √ - - - Pyranometer (1) Level 7 Insolation Wm-2 √ - - - Thermistor (1) Level 8 Air temperature 0C √ √ √ √ Barometer (1) Level 8 Air pressure atm √ √ √ √

Some of the leading wind farm developers such as ‘Wind Force’ and ‘SENOK’ also possess wind data, which have been measured from the anemometers located in their currently operating wind masts. However, those wind measurements are not reliable since it is always being disturbed by the long shafts of the wind turbine. Also due to the commercial competition between wind farm developers, generally they do not offer such data freely to the public.

3.3 Geographical Maps Available in the Survey Department of Sri Lanka

Survey Department of Sri Lanka possesses the topographical maps of Sri Lanka. Sri Lanka has been divided into 92 mapping zones with a scale of 1:50000 as shown in Figure 3.3. Those maps contain important geological features such as water bodies, cultivations, permanent structures, lagoons, etc. The price of a single map is 150 LKR.

Figure 3.3: Index to 1:50000 maps of Sri Lanka

3.4 Summary of the Chapter

As a summary there are three major institutions from where the required data for this study can be obtained. They are,

 Department of Meteorology Sri Lanka – Wind data is recorded at 10 m height above ground level, altogether in 24 major and automatic weather stations over a period of 35 years approximately. In major weather stations the wind speeds are being recorded manually as 3-minute averages once in every 3 hours while in AWS the wind speeds are being recorded automatically and continuously as instantaneous maximums.

 SLSEA – Wind data is freely available from three locations in Northern Province (Ponnalai, Pooneryn and Kokkilai) at several heights as 10-minute averages over a period of one year (23.02.2015 to 23.02.2016)

 Survey Department of Sri Lanka – Topographical maps with a scale of 1:50000 are available at a price of 150 LKR per each map.

4 DEVELOPMENT OF WIND LOADING MAP FOR SRI LANKA

4.1 Methodology

As the initial step, all of the wind data obtained can be converted in to a common unit. Then using past literature an equation can be deduced to predict the 3-second gust speed using the instantaneous maximums recorded in each AWS. This equation depends on the ‘p’ value of the surface which is a function of the surface roughness characteristics of the ground. The roughness lengths for each station can be determined using the recommended tables provided in the literature and the satellite images obtained from the Google Maps.

To convert the 3-minute average wind speeds in ROB to 3-second gusts the WMO guidelines can be used. To determine the gust conversion factors for each weather stations, they must be categorized as in-land, off-land, off-sea or at-sea. For that the satellite images obtained from the Google Maps can be used by considering the distribution of surface obstacles in the vicinity of the weather station.

The extreme wind speed for each weather station can be calculated for several return periods to both Gringorten and Gumbel methods. The weather stations that do not contain sufficient amount of wind data should be excluded from the analysis. One of the method from the Gringorten or Gumbel methods should be selected to develop the wind loading map. The obtained 3-second gust speeds can be converted into other averaging time periods such as 10-minutes average and hourly mean wind speeds using WMO guidelines.

The calculated extreme wind speeds from can be plotted using Surfer software and Kriging interpolation technique to generate the wind speed contours over a return period of 50 years. A district based wind loading map can be developed by analyzing the generated wind contours in each district.

Finally, the wind loading zones and their basic wind speeds for Sri Lanka can be developed based on the generated wind contours.

4.2 Kriging Interpolation for a Two Dimensional Spatial System

Kriging interpolation technique is widely used for predicting unknown values by interpolating known values in a two dimensional spatial system. It is an inverse distance weighting method. Compared to the other interpolation techniques such as linear or triangular interpolation this technique gives a smooth output.

Kriging technique depends on the semi-variogram. It means semi-variograms quantify the auto-correlation because it graphs out the variance of all pairs of data according to the distance. Chances of having a relation with the closer points are higher than the far points. Therefore, the closer points have a small semi-variance while the far points have a large semi-variance.

However, there are some limitations in this technique as mentioned below:

 The data set should follow a normal distribution  The data set should not have any trends (systematic changes)  The data set should be stationary (local variations does not change from the different areas of the map)

4.3 Conversion between 3-Second Gust and the Other Averaging Periods

Previously it has been discussed the difference between gust speeds for different averaging periods in Section 2.3. As previously discussed Hsu [13] developed and verified equations that can be used to predict 3-second gusts and instantaneous maximum wind speeds using 1-minute average wind speeds as shown in Equations 1 and 2. However, the Department of Meteorology of Sri Lanka measures neither 3- second gust nor 1-minute average wind speeds. They measure the maximum wind

speeds either as instantaneous maximums or as 3-minutes averages in AWS and major weather stations, respectively. Therefore, Equation 19 was derived using Equations 1 and 2 to determine the 3-second gust if the instantaneous maximum wind speed and the value of 푝′ (Exponent of the power law of wind profile) for a weather station is known. 푝′ is a function of the surface roughness of the ground as indicated in Equation 3.

(1 + 2푝′) 푈 = 푈 . (19) (3−푠푒푐) (푚푎푥) (1 + 3푝′)

Based on the roughness lengths (푍표) suggested by McRae et al. [15] (see Figure 2.8), the roughness lengths were assigned for each meteorological station in Sri Lanka. In this process the satellite images obtained from Google Maps were used to identify the

푍표 values based on the distribution of surface obstacles which are located in the vicinity of the measuring station. Thereafter Equation 3 suggested by Panofsky and Dutton [14] was used to calculate the 푝′ values for each meteorological station as listed in Table 4.1. Those results were used to predict the 3-second gust speed for each station using available instantaneous maximums.

Table 4.1: Assigned ‘푝′’ Values for each AWS

Automatic Weather 풑′ Automatic Weather 풑′ Station Station Anuradhapura 0.09 Mannar 0.07 Badulla 0.15 Monaragala 0.12 Bandarawela 0.15 Nuwara Eliya 0.15 Batticaloa 0.05 Polonnaruwa 0.09 Colombo 0.15 Pottuvil 0.05 Deniyaya 0.09 Puttalam 0.12 Hambantota 0.05 Rathmalana 0.15 Katugastota 0.15 Ratnapura 0.12 Katunayake 0.07 Tawalama 0.09 Kurunegala 0.12 Vavuniya 0.09 Maha Iluppallama 0.09

In major weather stations of Department of Meteorology, the wind speeds are being recorded as 3-minute averages. They were converted into 3-second gusts based on the recommendations of the WMO [16], indicated in Table 2.4. The gust conversion factors assigned for each major weather station are listed in Table 4.2. Even in this process, the satellite images obtained from Google Maps were used to categorize the exposure class of the surfaces based on the distribution of surface obstacles in the vicinity of the measuring station. The stations were categorized as in-land, off-land, off-sea or at-sea as shown in Table 4.2.

The gust conversion factors from 3-second gust to both 10-minute average and hourly mean for each weather station are also indicated in Table 4.2. To assign these values the same WMO guideline was used [16].

Table 4.2: Assigned gust conversion factors for each weather station based on WMO guidelines

Major Weather Exposure Assigned Gust Conversion Factor Station Class at From From From +10 m 3-Minute Average 3-Second Gust 3-Second Gust above to to to Ground 3-Second Gust 10-Minute Average 1-Hour Mean Anuradhapura In-land 1.58 1/1.66 1/1.75 Badulla In-land 1.58 1/1.66 1/1.75 Bandarawela In-land 1.58 1/1.66 1/1.75 Batticaloa Off-sea 1.31 1/1.38 1/1.45 Colombo In-land 1.58 1/1.66 1/1.75 Deniyaya In-land 1.58 1/1.66 1/1.75 Galle Off-sea 1.31 1/1.38 1/1.45 Hambantota Off-land 1.44 1/1.52 1/1.60 Jaffna In-land 1.58 1/1.66 1/1.75 Katugastota In-land 1.58 1/1.66 1/1.75 Katunayake Off-land 1.44 1/1.52 1/1.60 Kurunegala In-land 1.58 1/1.66 1/1.75 Maha Iluppallama In-land 1.58 1/1.66 1/1.75 Mannar In-land 1.58 1/1.66 1/1.75 Monaragala In-land 1.58 1/1.66 1/1.75 Nuwara Eliya In-land 1.58 1/1.66 1/1.75 Polonnaruwa In-land 1.58 1/1.66 1/1.75 Pottuvil Off-land 1.44 1/1.52 1/1.60 Puttalam In-land 1.58 1/1.66 1/1.75 Rathmalana In-land 1.58 1/1.66 1/1.75 Ratnapura In-land 1.58 1/1.66 1/1.75 Tawalama In-land 1.58 1/1.66 1/1.75 Trincomalee Off-sea 1.31 1/1.38 1/1.45 Vavuniya In-land 1.58 1/1.66 1/1.75

4.4 Determination of Extreme Wind Speeds

Initially, all the wind data obtained were converted into SI units. Most of them were recorded in either kmph, mph or Knots. Afterwards all of them were converted in to 3-second gust, which were initially in different formats such either as 3-minute averages or as instantaneous maximums using the methods described in Section 4.3.

Both the Gringorten and Gumbel methods were used to predict the extreme wind speeds for several return periods (5, 10, 50, 100, 200, 500 and 1000 years) at each weather station (see Tables 4.3 and 4.4).

Table 4.3: Predicted extreme wind speeds using Gringorten method for each weather station and for several return periods

Weather Station Predicted Extreme Wind Speeds (ms-1) 3-Second Gust 10-Minute Average 1-Hour Mean Return Period Return Period Return Period (Years) (Years) (Years) 5 10 50 100 200 500 1000 5 10 50 100 200 500 1000 5 10 50 100 200 500 1000 Anuradhapura 24.9 26.9 31.4 33.3 35.2 37.7 39.7 15.0 16.2 18.9 20.0 21.2 22.7 23.9 14.3 15.4 17.9 19.0 20.1 21.6 22.7 Badulla 27.9 30.6 36.7 39.3 41.9 45.4 48.1 16.8 18.4 22.1 23.7 25.3 27.4 28.9 16.0 17.5 21.0 22.5 24.0 26.0 27.5 Bandarawela 30.0 32.4 37.8 40.1 42.4 45.5 47.8 18.1 19.5 22.7 24.1 25.5 27.4 28.8 17.2 18.5 21.6 22.9 24.2 26.0 27.3 Batticaloa 44.1 47.8 56.3 59.9 63.6 68.4 72.1 32.0 34.6 40.8 43.4 46.1 49.6 52.2 30.4 32.9 38.8 41.3 43.9 47.2 49.7 Colombo 24.4 26.9 32.8 35.3 37.9 41.2 43.7 14.7 16.2 19.8 21.3 22.8 24.8 26.3 13.9 15.4 18.7 20.2 21.6 23.5 25.0 Deniyaya 22.2 24.8 30.9 33.5 36.1 39.6 42.2 13.4 14.9 18.6 20.2 21.8 23.8 25.4 12.7 14.2 17.6 19.1 20.6 22.6 24.1 Galle 30.2 32.0 36.2 38.0 39.8 42.2 44.0 21.9 23.2 26.2 27.5 28.8 30.6 31.9 20.8 22.1 25.0 26.2 27.5 29.1 30.3 Hambantota 32.8 35.4 41.3 43.9 46.4 49.8 52.3 21.6 23.3 27.2 28.9 30.5 32.8 34.4 20.5 22.1 25.8 27.4 29.0 31.1 32.7 Jaffna 16.9 17.6 19.0 19.6 20.2 21.0 21.6 10.2 10.6 11.4 11.8 12.2 12.7 13.0 9.7 10.0 10.8 11.2 11.5 12.0 12.3 Katugastota 23.3 24.4 27.1 28.2 29.4 30.9 32.0 14.0 14.7 16.3 17.0 17.7 18.6 19.3 13.3 14.0 15.5 16.1 16.8 17.7 18.3 Katunayake 30.6 31.4 33.2 33.9 34.7 35.7 36.5 20.1 20.6 21.8 22.3 22.8 23.5 24.0 19.1 19.6 20.7 21.2 21.7 22.3 22.8 Kurunegala 28.8 30.2 33.6 35.1 36.6 38.5 40.0 17.3 18.2 20.3 21.2 22.0 23.2 24.1 16.4 17.3 19.2 20.1 20.9 22.0 22.8 Maha Iluppallama 38.0 38.9 40.9 41.8 42.7 43.9 44.7 22.9 23.4 24.7 25.2 25.7 26.4 27.0 21.7 22.2 23.4 23.9 24.4 25.1 25.6 Mannar 36.2 37.8 41.7 43.4 45.0 47.2 48.9 21.8 22.8 25.1 26.1 27.1 28.4 29.5 20.7 21.6 23.8 24.8 25.7 27.0 27.9 Monaragala 21.5 23.5 28.2 30.3 32.3 35.0 37.0 13.0 14.2 17.0 18.2 19.5 21.1 22.3 12.3 13.5 16.1 17.3 18.5 20.0 21.1 Nuwara Eliya 36.5 37.7 40.5 41.7 42.9 44.5 45.7 22.0 22.7 24.4 25.1 25.8 26.8 27.5 20.8 21.5 23.1 23.8 24.5 25.4 26.1 Polonnaruwa 23.2 24.6 27.7 29.0 30.4 32.1 33.5 14.0 14.8 16.7 17.5 18.3 19.4 20.2 13.3 14.0 15.8 16.6 17.3 18.4 19.1 Pottuvil 24.0 25.9 30.4 32.3 34.3 36.8 38.8 15.8 17.0 20.0 21.3 22.5 24.2 25.5 15.0 16.2 19.0 20.2 21.4 23.0 24.2 Puttalam 22.8 24.7 28.9 30.7 32.5 34.9 36.7 13.8 14.9 17.4 18.5 19.6 21.0 22.1 13.1 14.1 16.5 17.5 18.6 20.0 21.0 Rathmalana 26.4 28.6 33.6 35.8 38.0 40.9 43.1 15.9 17.2 20.3 21.6 22.9 24.6 25.9 15.1 16.3 19.2 20.5 21.7 23.4 24.6 Ratnapura 42.5 44.7 49.6 51.8 53.9 56.7 58.8 25.6 26.9 29.9 31.2 32.5 34.2 35.4 24.3 25.5 28.4 29.6 30.8 32.4 33.6 Tawalama 23.9 26.3 31.9 34.3 36.7 39.9 42.2 14.4 15.8 19.2 20.7 22.1 24.0 25.5 13.7 15.0 18.2 19.6 21.0 22.8 24.1 Trincomalee 42.2 45.5 53.2 56.5 59.8 64.2 67.5 30.6 33.0 38.6 41.0 43.3 46.5 48.9 29.1 31.4 36.7 39.0 41.3 44.3 46.5 Vavuniya 21.5 23.6 28.3 30.4 32.4 35.2 37.2 13.0 14.2 17.1 18.3 19.5 21.2 22.4 12.3 13.5 16.2 17.4 18.5 20.1 21.3

Table 4.4: Predicted extreme wind speeds using Gumbel method for each weather station and for several return periods

Weather Station Predicted Extreme Wind Speeds (ms-1) 3-Second Gust 10-Minute Average 1-Hour Mean Return Period Return Period Return Period (Years) (Years) (Years) 5 10 50 100 200 500 1000 5 10 50 100 200 500 1000 5 10 50 100 200 500 1000 Anuradhapura 25.5 27.5 32.2 34.2 36.2 38.9 40.9 15.4 16.6 19.4 20.6 21.8 23.4 24.7 14.6 15.7 18.4 19.6 20.7 22.2 23.4 Badulla 29.6 32.8 40.2 43.4 46.6 50.8 54.0 17.8 19.8 24.2 26.2 28.1 30.6 32.5 16.9 18.8 23.0 24.8 26.6 29.0 30.9 Bandarawela 30.9 33.3 39.1 41.6 44.0 47.3 49.8 18.6 20.1 23.5 25.0 26.5 28.5 30.0 17.6 19.1 22.3 23.7 25.2 27.0 28.4 Batticaloa 47.5 51.8 61.8 66.0 70.3 76.0 80.2 34.4 37.5 44.8 47.9 51.0 55.1 58.2 32.8 35.7 42.6 45.5 48.5 52.4 55.3 Colombo 25.0 27.7 33.8 36.5 39.1 42.6 45.3 15.1 16.7 20.4 22.0 23.6 25.7 27.3 14.3 15.8 19.3 20.8 22.4 24.4 25.9 Deniyaya 24.3 27.9 36.3 39.9 43.5 48.3 51.9 14.7 16.8 21.9 24.1 26.2 29.1 31.3 13.9 16.0 20.8 22.8 24.9 27.6 29.7 Galle 32.3 34.5 39.5 41.7 43.9 46.8 49.0 23.4 25.0 28.6 30.2 31.8 33.9 35.5 22.2 23.8 27.3 28.8 30.3 32.3 33.8 Hambantota 33.8 36.5 42.9 45.6 48.3 51.9 54.7 22.2 24.0 28.2 30.0 31.8 34.2 36.0 21.1 22.8 26.8 28.5 30.2 32.5 34.2 Jaffna 17.8 18.6 20.4 21.2 21.9 22.9 23.7 10.7 11.2 12.3 12.7 13.2 13.8 14.3 10.2 10.6 11.6 12.1 12.5 13.1 13.6 Katugastota 23.9 25.2 28.2 29.5 30.8 32.5 33.8 14.4 15.2 17.0 17.8 18.5 19.6 20.3 13.6 14.4 16.1 16.8 17.6 18.6 19.3 Katunayake 31.4 32.4 34.7 35.7 36.7 38.0 39.0 20.6 21.3 22.8 23.5 24.1 25.0 25.6 19.6 20.2 21.7 22.3 22.9 23.7 24.4 Kurunegala 29.9 31.8 36.4 38.4 40.3 42.9 44.9 18.0 19.2 21.9 23.1 24.3 25.9 27.1 17.1 18.2 20.8 21.9 23.1 24.5 25.7 Maha Iluppallama 38.8 40.0 42.8 44.0 45.3 46.9 48.1 23.4 24.1 25.8 26.5 27.3 28.2 29.0 22.2 22.9 24.5 25.2 25.9 26.8 27.5 Mannar 38.1 40.3 45.2 47.4 49.5 52.3 54.5 23.0 24.3 27.2 28.5 29.8 31.5 32.8 21.8 23.0 25.8 27.1 28.3 29.9 31.1 Monaragala 23.0 25.5 31.3 33.8 36.3 39.5 42.0 13.8 15.3 18.8 20.3 21.8 23.8 25.3 13.1 14.6 17.9 19.3 20.7 22.6 24.0 Nuwara Eliya 37.3 38.9 42.5 44.0 45.6 47.6 49.2 22.5 23.4 25.6 26.5 27.5 28.7 29.6 21.3 22.2 24.3 25.2 26.1 27.2 28.1 Polonnaruwa 24.1 25.7 29.1 30.6 32.1 34.1 35.6 14.5 15.5 17.6 18.5 19.4 20.6 21.5 13.8 14.7 16.7 17.5 18.4 19.5 20.4 Pottuvil 24.9 27.0 31.9 34.0 36.1 38.9 41.0 16.4 17.8 21.0 22.4 23.7 25.6 27.0 15.6 16.9 19.9 21.2 22.6 24.3 25.6 Puttalam 23.2 25.1 29.4 31.3 33.2 35.7 37.6 14.0 15.1 17.7 18.9 20.0 21.5 22.6 13.3 14.3 16.8 17.9 19.0 20.4 21.5 Rathmalana 27.6 30.2 36.1 38.7 41.2 44.6 47.1 16.6 18.2 21.8 23.3 24.8 26.9 28.4 15.8 17.3 20.6 22.1 23.6 25.5 26.9 Ratnapura 44.7 47.6 54.4 57.3 60.2 64.0 67.0 26.9 28.7 32.8 34.5 36.3 38.6 40.3 25.5 27.2 31.1 32.7 34.4 36.6 38.3 Tawalama 25.9 29.0 36.1 39.1 42.2 46.2 49.3 15.6 17.5 21.7 23.6 25.4 27.8 29.7 14.8 16.6 20.6 22.4 24.1 26.4 28.1 Trincomalee 46.5 50.6 60.1 64.2 68.3 73.7 77.7 33.7 36.7 43.6 46.5 49.5 53.4 56.3 32.1 34.9 41.5 44.3 47.1 50.8 53.6 Vavuniya 23.5 26.1 32.2 34.8 37.4 40.9 43.5 14.1 15.7 19.4 21.0 22.6 24.6 26.2 13.4 14.9 18.4 19.9 21.4 23.4 24.9

The Gringorten method is a more fitting and unbiased method than the Gumbel method for extreme wind speed predictions [18] & [20]. The least square method (Linear regression) was used to estimate the best fit linear prediction for each station. The calculated RMSE2 values for both methods are listed in Table 4.5.

Table 4.5: RMSE2 values for each station for both Gringorten and Gumbel methods

RMSE2 (R2) Weather Station Gringorten Method Gumbel Method Anuradhapura 0.9155 0.9019 Badulla 0.9701 0.9654

Bandarawela 0.9766 0.9746

Batticaloa 0.9242 0.9462 Colombo 0.9745 0.9735 Deniyaya 0.9887 0.9761 Galle 0.8605 0.8312 Hambantota 0.9806 0.9786

Jaffna 0.9364 0.9518 Katugastota 0.9610 0.9622 Katunayake 0.8042 0.8357 Kurunegala 0.9784 0.9903 Maha Iluppallama 0.9978 0.9910 Mannar 0.8561 0.8937

Monaragala 0.9724 0.9492 Nuwara Eliya 0.9580 0.9782 Polonnaruwa 0.8090 0.8204 Pottuvil 0.9797 0.9807 Puttalam 0.7798 0.7465

Rathmalana 0.9739 0.9523 Ratnapura 0.8358 0.8754 Tawalama 0.9512 0.9555 Trincomalee 0.8922 0.8698 Vavuniya 0.9638 0.9382

These extreme value distributions are derived for unbiased samples of continuously recorded gust speeds. However, since the 3-minute averages recorded in ROB are being recorded only once in every 180 minutes (3 hours) a correction should be required. This means that the monthly maximum wind speeds that were measured have actually been recorded over an equivalent period of half a day (30 days × 3 / 180). In order to complete an actual monthly record, 60 such half a day periods had to be considered.

As the initial step of above correction procedure, the total number of monthly maximums (N) in ROB data set were sorted in the ascending order and the largest N/60 items were considered as the corrected number of monthly maximums. Afterwards the continuously recorded AWS monthly maximums were added to above dataset to increase its size. The error that could occur as a result of recording discontinuous data was avoided by using this method. It was assumed that the recorded 3-minute average wind speeds are unbiased samples. Weather stations that did not contain sufficient amount of wind data were excluded from the analysis such as Mattala.

Figures 4.1-4.3 contain some samples of the graphs which were plotted to predict the extreme wind speeds using both the Gringorten and Gumbel method for several stations. The rest of the graphs which were plotted to using both Gringorten and Gumbel methods are indicated in Appendix – B of this report. The top end of the predicted straight line is the critical part while predicting the extreme wind speeds for a given return period. If the top end of the predicted straight line deviates from the recorded maximums, the straight line has been adjusted as shown in Figure 4.2. That modified line may not be coincided with the bottom left part of the curve which is not so important while predicting the extreme wind speeds.

35 ---- Gumbel Method

) 1 - 30 y = 3.563x + 16.295 R² = 0.9746 25 __ Gringorten Method 20 y = 3.3474x + 16.348 15 R² = 0.9766 10

5 Ranked Wind Speed (ms Speed Wind Ranked 0 -2 -1 0 1 2 3 4 5 For Gringorten Method the Axis is: -ln{-ln[(m-0.44)/(N+0.12)]} For Gumbel Method the Axis is: -ln{-ln[(m)/(N+1)]}

Figure 4.1: Application of Gringorten and Gumbel methods for Bandarawela station (Original in Colour)

50 ---- Gumbel Method )

1 45 - y = 4.7151x + 22.054 40 R² = 0.9744 35 30 ...... Gringorten Method 25 (Modified Trendline) 20 __ Gringorten Method 1.7399x + 29.349 R² = 0.958 15 y = 4.436x + 22.134 10 R² = 0.9806

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 5 6 For Gringorten Method the Axis is: -ln{-ln[(m-0.44)/(N+0.12)]} For Gumbel Method the Axis is: -ln{-ln[(m)/(N+1)]}

Figure 4.2: Application of Gringorten and Gumbel methods for Nuwara Eliya station (Original in Colour)

40 ---- Gumbel Method

) 1 - 35 y = 3.9295x + 17.747 30 R² = 0.9786 25 __ Gringorten Method y = 3.6754x + 17.809 20 R² = 0.9806 15 10

5 Ranked Wind Speed (ms Speed Wind Ranked 0 -2 -1 0 1 2 3 4 5 For Gringorten Method the Axis is: -ln{-ln[(m-0.44)/(N+0.12)]} For Gumbel Method the Axis is: -ln{-ln[(m)/(N+1)]}

Figure 4.3: Application of Gringorten and Gumbel methods for Hambantota station (Original in Colour)

4.5 Generating of the Wind Loading Map

Both the Gringorten and Gumbel methods have shown closer results for the extreme wind speeds, but the Gumbel method has shown slightly higher values. Since the Gringorten method is a more fitting and unbiased method than the Gumbel method [18] it was selected for the generation of wind contours.

The calculated extreme wind speed values from Gringorten method for 24 weather stations were plotted using Surfer software to generate wind speed contours. 3-Second gusts and 10-minute averages for a return period of 50 years were selected for plotting. The Kriging interpolation technique was adopted to interpolate and extrapolate the wind speeds between the weather stations. The plotted wind speed contours for 3- second gust and 10-minute average wind speeds are shown in Figures 4.4 and 4.5 respectively. The considered return period is 50 years.

Similarly, district based wind zones were developed for 3-second gust and 10-minute average wind speeds based on the generated wind contours (see Figures 4.6 and 4.7). These maps were generated such that the wind speed values indicated in each district are not being exceeded more than 5% from the total area of the district.

Basic Wind Speed Contours for Sri Lanka

-1 All wind speeds are in ms as 3-second gusts 34

50 46 42 50 38

Figure 4.4: Basic wind speed contours for Sri Lanka in 3-second gust wind speeds (Colour in Original)

Basic Wind Speed Contours for Sri Lanka

All wind speeds are in ms-1

20 as 10-minute averages

32 35 29

26 23

20 20

Figure 4.5: Basic wind speed contours for Sri Lanka in 10-minute average wind speeds (Colour in Original)

District Based Wind Loading 31 Zones for Sri Lanka

All wind speeds are in ms-1 as 3-second gusts 34

37 33 50

46 48 42 37 38

34 32 36

34 31

33 40 35 38 40 37 35 36 36 40

39 34 34

Figure 4.6: District based wind loading zones for Sri Lanka in 3-second gust wind speeds (Colour in Original)

District Based Wind Loading 22 Zones for Sri Lanka

All wind speeds are in ms-1 as 10-minute averages 23

24 24 35 32 35 29

26 26 23

20 21 29

22 20 35

23 30 22 25 25 25 23 24

22 26

25 27 25

Figure 4.7: District based wind loading zones for Sri Lanka in 10-minute average wind speeds (Colour in Original)

New wind loading zones for Sri Lanka was introduced (see Figure 4.8) based on the wind contours generated in map shown in Figure 4.5. For the convenience of the users, three major wind loading zones have been proposed. Based on the pattern of the wind contours, basic wind speed values for each zone has been assigned for several return periods. The basic wind speed values for each zone for different return periods are given in Table 4.6.

Table 4.6: Proposed basic wind speeds for each wind loading zone

Basic Wind Speed (ms-1) 3-Second Gust 10-Minute Average Hourly Mean Return Period (Years) 100 200 500 1000 100 200 500 1000 100 200 500 1000 Wind 5 10 50 5 10 50 5 10 50 Loading Zone I 40 43 50 54 57 61 64 28 31 35 38 40 43 45 26 29 33 36 38 41 43 II 34 36 42 45 47 50 53 23 25 29 31 33 35 37 21 23 27 29 31 33 35 III 32 34 38 40 42 44 46 20 21 23 24 25 26 27 19 20 21 22 22 23 24

Compared to the existing map, the proposed map shown in Figure 4.8 is having a similar variation in Eastern coastal region. However, the coastal region in the Southern province, Mannar Island and some regions in Sabaragamuwa Province show higher wind speeds than the values indicated in the existing map. Also the minimum design wind speed for Sri Lanka has been increased from 33 ms-1 to 38 ms-1.

Wind Loading Zones Jaffna for Sri Lanka

Kilinochchi Mullaitivu Wind

Zone Zone 2 1 Mannar 2 Vavuniya

Trincomalee 3 Zone Zone 2 Anuradhapura 1

Puttalam

Polonnaruwa

Zone Batticaloa 3 Matale Kurunegala Kandy Kandy Kegalle Ampara Nuwara Gampaha Eliya Badulla Badulla Nuwara Monaragala ColomboColomb Monara Zone Ratnapu Zone Ratnapura 2

KalutaraKalutara 3

Zone Hamban2 Hambantota

Galle Galle Reference: W. L. S. Maduranga and C. S. Matara Lewangamage, “An Assessment of Wind Loading and Matara Wind Energy Potential for Sri Lanka”, Department of Civil Engineering, University of Moratuwa, 2019.

Figure 4.8: Proposed wind loading zones for Sri Lanka (Colour in Original)

4.6 Proposed Modification to the Generated Map

The past cyclone paths have been clearly indicated that the coastal regions of Jaffna and Mullaitivu districts are vulnerable for the cyclone attacks which can be originated in the Bay of Bengal (see Figure 2.2). However, in the proposed map, those regions are located in the lowest critical zone. The reason for this may be the poor data availability in those regions during the past 35 years. The Kriging interpolation technique basically performs a spatial interpolation between points. Therefore, if there are points with unrealistic values, it is possible that the regions closer to those points may show some unrealistic values as well.

In the Indian wind loading map shown in Figure 2.5, the coastal margin closer to the Bay of Bengal has a basic wind speed which is equal to 50 ms-1. Also in the proposed map for Sri Lanka, the Eastern coast contains a value equals to 50 ms-1 as the basic wind speed. By considering above facts and by analyzing the surface characteristics of those regions observed from the Google Maps, it can be proposed some modification for the wind loading zones. The coastal regions which are closer to the Bay of Bengal and which have less surface obstacles and located almost in a flat terrain can be considered as critical zones.

4.6 Summary of the Chapter

To develop the wind loading map for Sri Lanka, initially all the wind data obtained was converted in to ms-1. An equation was deduced to predict the 3-second gust speed using the instantaneous maximums recorded in each AWS. This equation depends on the ‘p’ value of the surface which is a function of the surface roughness characteristics of the terrain. The roughness lengths for each station were assigned using the recommended tables and the satellite images obtained from the Google Maps.

To convert the 3-minute average wind speeds in ROB to 3-second gusts the WMO guidelines were used. To determine the gust conversion factors for each weather stations, they were categorized as in-land, off-land, off-sea or at-sea by analyzing the satellite images obtained from the Google Maps.

The extreme wind speed for each weather station was calculated for several return periods (5, 10, 50, 100, 200, 500 and 1000 years) to both Gringorten and Gumbel methods. The weather stations that did not contain sufficient amount of wind data were excluded from the analysis. The results obtained from Gumbel method were slightly higher than the results obtained from the Gringorten method. The obtained results from the Gringorten method were selected to develop the wind loading map, because it is a more fitting and unbiased method compared to the Gumbel method. Those values were converted into 10-minutes average and hourly mean wind speeds using WMO guidelines.

The calculated extreme wind speeds from 24 weather stations were plotted using Surfer software and Kriging interpolation technique to generate the wind speed contours for both 3-second gust and 10-minute averages over a return period of 50 years. Similarly, district based wind loading map was developed for 3-second gust and 10-minute averages based on the generated wind contours.

As a result, three wind loading zones and their basic wind speeds for Sri Lanka were introduced based on the generated wind contours. Compared to the existing map, the

proposed map has a similar pattern in Eastern coastal region. However, the coastal region in the Southern province, Mannar Island and some regions in Sabaragamuwa Province show higher wind speeds than the values indicated in the existing map. Also the basic wind speed value for each zone has been increased slightly.

The proposed wind loading zones which are developed by based on the wind contours have shown some unrealistic lower wind speeds in coastal region of Northern Province may be due to the lack of past data records in those regions. Past cyclone paths have provided evidences for this ambiguity. Therefore, some modifications to zone margins can be done further by considering the past cyclone paths, the proximity of the locations to the Bay of Bengal and their nature of the surface observed from the Google Maps.

5 DEVELOPMENT OF WIND POWER DISTRIBUTION MAP

5.1 Methodology

As the initial step, a suitable region for the analysis should be identified by considering the facts such as wind data availability, wind energy potential and power grid availability of that region.

By careful inspection the blunders in the wind data set should be removed manually before starting the analysis. The average speeds of topmost two anemometers must be used for the analysis, because those are the least disturbed anemometers at the wind mast.

WAsP is a commercially available CFD based software package which can be used to analyze wind data. The sequence of feeding the data to this software is mentioned below.

 Import the images of 1:50000 scaled topographical maps.  Carry out the coordinate conversion and digitize the map  Apply the position and dimensions of the surface obstacles  Assign surface roughness coefficients  Model the terrain by demarcating the contour lines  Define the boundary conditions  Input the wind data of the reference site  Feed the locations of those wind masts

The generated maps can be modified using MATLAB software to obtain maps with high resolutions.

5.2 Identifying a Suitable Region for the Study

The major limitation to develop a wind power distribution map for the entire country is the scarcity of required wind data. However, there is no point of developing such map for the entire country, because several previous studies have already identified the wind energy potential zones in macro scale level (see Section 2.5). On the other hand, accurate modeling of entire country is a difficult and time consuming process. Even if it is possible it will take much longer time to run that model in a computer.

The Jaffna Peninsula region was selected for the study because it is the only region where the required wind data is feely available. Also it has been identified that the Northern province as one of the highest wind energy potential region in Sri Lanka.

5.3 Procedure to Generate the Wind Power Distribution Map Using WAsP

WAsP software package was used for the generation of wind power distribution maps. The algorithms which are used in WAsP have already been tested and applied for more than 20 years for several locations in the world.

To obtain accurate results from WAsP the wind data should be extrapolated within a range of 50 km for a flat terrain. Hence, the map numbers 1 (Manipay), 2 (Point Pedro), 3 (Jaffna), 4 (Chavakachcheri) and 8 (Kilinochchi) were selected for the analysis to be used with the data available in Ponnalai and Pooneryn stations (see Figure 5.1).

Map No. 1 Map No. 2 (Manipay) (Point Pedro)

Ponnalai

Pooneryn

Map No. 3 Map No. 4

(Jaffna) (Chavakachcheri)

Map No. 8 (Kilinochchi)

Figure 5.1: Selected 1:50000 scaled survey maps and wind measuring stations for the analysis (Colour in Original)

The Figure 5.2 represents a schematic diagram of the wind atlas development procedure in WAsP. In the analysis section (upward arrow), the weather models are used to determine the regional wind climatology from the recorded wind data. In the reverse process, the application of wind atlas data (downward arrow), the wind climate at any site will be determined from the regional climatology.

Figure 5.2: Wind atlas development procedure in WAsP [25]

Generally, for accurate predictions using WAsP, following conditions should be satisfied [25].

 The reference site (data obtaining station) and the predicted site (location of wind farm) should be subjected to same overall weather conditions  The reference wind data should be reliable  The prevailing meteorological conditions should be neutrally stable approximately  The topographical model inputs should be reliable and adequate  The surrounding ground (of both reference and predicted sites) should be sufficiently smooth and gentle

In the selection of wind data, number of conditions should be satisfied as mentioned below.

 Sufficient time period of data should be available (at least one year, generally several years are preferred).  Accurate description of anemometer conditions and data of hourly or 10-minute averages should be collected.  Well exposed anemometers should be used which are far from the buildings and other kind of obstacles. This is the requirement most difficult to satisfy.

The data obtained for this research is from a period of one-year (from 23.02.2015 to 23.02.2016) and the anemometers are undisturbed as much as possible (see Figures A.1 - A.3). All the data has been recorded as 10-minute averages and the accuracy of all the parameters measured is for two decimal places in SI units. On the mast altogether six anemometers were set to remove the error causing due to the disturbance of the mast itself for the wind measurements. Also it is a precaution in cases such as anemometer failures.

Time to time variations which can be occurred in terrain and its surface obstacles after the preparation of the 1:50000 scaled survey maps were not considered in this analysis.

5.3.1 Basic principles and mathematical models used in WAsP

The fundamentals of WAsP are some mathematical equations which are time consuming or impossible to solve manually in large scale problems. With the massive development in IT infrastructures, nowadays the superfast computers are capable to perform enormous amount of mathematical operations within a glace of time with a higher accuracy. WAsP is basically a CFD based program. The solutions for the differential equations in CFD based problems are basically numerical solutions. Generally, the numerical solutions require iteration processes which can be easily accomplished with an aid of a computer. The procedures and equations used in WAsP for wind power analysis are described below.

In WAsP the total power production is calculated as the sum of the power production of each sector. Once the power curve 푃(푢) is measured for a wind turbine, the average power production can be estimated using the probability density function of wind speed at the hub height as indicated in Equation 20 [30].

∞ 푃 = ∫ Pr(푢). 푃(푢) . 푑푢 (20) 0

Where, 푃 = Mean power production

The wind speeds at hub height are determined either by a siting procedure or measurements. If the probability density function Pr(푢) has been determined through a siting procedure, it is given as a Weibull function. In this case the expression for the mean power production is shown in Equation 21 [30].

∞ 푘 푢 푘−1 푢 푘 푃 = ∫ ( ) ( ) exp {− ( ) } 푃(푢) . 푑푢 (21) 퐴 퐴 퐴 0

Where, 푃 = Mean power production

Generally, this integral function cannot be computed analytically. Therefore, numerical methods must be adopted for the calculation. Actual power curves are smooth and they can be approximated accurately by a piece by piece using a linear function with few numbers of nodes. Using this approximation, the power curve can be written as shown in the Equation 22 [30]. This is just a linear interpolation between two known nodes to determine the power at the unknown node.

푃푖+1 − 푃푖 푃(푢) = (푢 − 푢푖) + 푃푖 (22) 푢푖+1 − 푢푖

Using this simplified method an analytical solution for Equation 21 has been obtained by Petersen et al. [31] as indicated in Equation 23.

푃푖+1 − 푃푖 푃 = ∑ { [퐺푘(훼푖+1) − 퐺푘(훼푖)]} (23) 훼푖+1 − 훼푖 푖

Where, 푃 = Mean power production 푢 훼 = 푖 푖 퐴

By using Equation 23 the mean power production can be theoretically calculated by seperating that into a finite number of linear intervals. Generally, this method is only useful if the power curve can be written by a small number of linear intervals.

5.3.2 Feeding the data to the software

Before feeding the data to WAsP it has been carefully investigated for blunders such as abnormally high wind speeds. Using visual inspection of the time series of wind speed and direction and inspection of the frequency tables (histograms) several such

abnormal data records were caught. Those deficiencies were rectified by straight away omitting them manually. One of the drawback of this method is there is no simple method for filling missing data. However, compared to the huge number of accurate observations the number of few omitted data records can be neglected. The patterns created by data discretization can be omitted through WAsP software itself.

The sequence of feeding the data to the software is mentioned below.

5.3.3 Defining the coordinates

The maps which are inserted to WAsP are just images. Therefore, it does not have any idea about its scale or orientation until three known coordinates are introduced to the map. These three known coordinates were found from Google Earth software which were defined according to the GPS coordinate system. Those were needed to be converted to Kandawala coordinate system in which the coordinates are measured from meters. The World Coordinate Converter (online) was used for this coordinate conversion as sown in Figure 5.3.

Figure 5.3: Conversion of GPS coordinates to Kandawala coordinate system using the World Coordinate Converter (online) (Original in Colour)

The converted coordinates were inserted to the map as shown in Figure 5.4.

Figure 5.4: Sample of coordinates defined maps (Original in Colour)

5.3.4 Modelling the topography of the region

In order to determine the effects of the topography on the wind at a given place, it is necessary to describe the characteristics of the surroundings. There are three main effects of topography on the wind. They are,

 Surface roughness  Orography  Sheltering effect

In nature these effects are not entirely independent. However, WAsP allows the user to specify the sheltering obstacles, the roughness of the surrounding terrain and the orography independently.

5.3.4.1 Surface roughness

The collective effect of the surface obstacles and terrain, leads to an overall retardation of the wind closer to the ground surface, is called as the surface roughness of the terrain. Its influence on the wind flow is more significant than from the orography and the sheltering effect. Therefore, even the minor variations of surface roughness were considered as much as possible. If the surface roughness is not specified, the software will automatically assign a default roughness length as 0.03 m. By considering the mass scale size of the region it is difficult to model each and every obstacle. Therefore, the effects due to surface obstacles were included by embedding in the surface roughness.

Each detail in topographical maps such as cultivations, water bodies, etc. were assigned with relevant surface roughness values as indicated in Table 2.6.

5.3.4.2 Orography

Orographic elements such as hills, valleys, cliffs, escarpments and ridges exert an additional influence on the wind. Near the summit or crest of these features the wind will accelerate while near the foot and in valleys it will decelerate. Since the Jaffna region is a coastal region, it does not have any significant variation in orography compared to the other regions in the country. However, the orography of the terrain was defined by demarcating the contour lines.

5.3.4.3 Sheltering effect

The wind is strongly influenced closer to the obstacles. This effect extends in the vertical direction approximately thrice the height of the surface obstacle, and in the horizontal downstream approximately 30 - 40 times the height. If the points of intersection are located inside this influenced zone, it is necessary to consider the effects of the sheltering obstacles in to account. However, if the points are located outside the influenced zone, those obstacles can be embedded in the surface roughness. Most of the surface obstacles located in Jaffna region are single storey building with less than a height of 4 m. For wind power production the considered height is more than 40 m. Therefore, the effect due to the surface obstacles in such heights is negligible.

5.3.5 Defining roughness characteristics in WAsP

The lines separating surfaces which having different roughness are called as roughness change lines. Each roughness change line was assigned with two values for its both sides as shown in Figure 5.5.

0.0002

0.005 0.008

0.000

Figure 5.5: Defining of surface roughness in WAsP

These lines must be constructed more carefully to establish a consistent roughness map. The roughness change lines should not intersect each other at anywhere of the map. The digitized maps after assigning the surface roughness coefficients are displayed in Figure 5.6.

Figure 5.6: Digitized maps after defining the surface roughness values (Original in Colour)

5.3.6 Feeding the wind data to WAsP

10-minute average wind speeds and directions of topmost anemometers and wind vanes were fed to the software. To minimize the error which can be occurred due to the disturbance of wind mast itself the mean values of two anemometers at the topmost locations were considered.

5.4 Generation of Maps

Estimation of the mean energy of the wind over a large area is called as regional assessment and the prediction of the average yearly energy production of a specific location is called siting. The information necessary for siting generally needs to be much more detailed than in the case of regional assessment. However, both

applications make use of the general concepts of topography analysis and regional wind climatology.

Generation of high resolution maps takes more time for the analysis process. Hence a resolution of 900 m x 900 m (30” x 30”) is used.

Once the map is generated the estimated wind power values will be displayed on the map itself using a colour code. The wind power distribution maps for Jaffna Peninsula region were generated using WAsP to illustrate the annual variation of wind. Those maps for each zone are shown in the Figure 5.7.

Figure 5.7: Wind power distribution map generated by WAsP (Original in Colour)

The MATLAB software was used to increase the resolutions of the map up to 150 m x 150 m (5” x 5”) as shown in Figure 5.8. The other Sri Lankan wind resource maps have used a less resolution than this as indicated in Table 2.7. The water bodies were excluded from the map using image processing functions in MATLAB. Also the existing transmission line of 132 kW was included in the map.

Wind Power Distribution Map for Jaffna Peninsula Region Alvai Kankesanturai

Ponnalli Khadu

Karainagar Ampan

Kayts

Mandaitivu

Pooneryn Punkudutivu

Wind power density at 50 m height in Wm-2 > 500 470 - 500 440 - 470 410 - 440 360 - 410

300 - 360 Veravil 200 - 300 < 200

Reference: W. L. S. Maduranga and C. S. Lewangamage, “An Assessment of Wind Loading and Wind Energy Potential for Sri Lanka”, Department of Civil Engineering, University of Moratuwa, 2019. Figure 5.8: Finalized wind power distribution map after increasing the resolutions using MATLAB software (Original in Colour)

The regions which have the highest wind energy potential are listed below. The Islands which have not been connected by the road network such as Nainativu, Analaitivu and Eluvaitivu were excluded from the list.

 Veravil (Western coast)  Pooneryn (Topmost part of the peninsula)  Ampan (Eastern coast)  Punkudutivu (Southern and Western coast of the Island)  Kayts (Southern and Western coast of the Island)  Kankesanturai (Northern coast)  Ponnalli Khadu (Western coast)  Karainagar (North-Western coast of the Island)  Mandaitivu (Southern coast of the Island)  Alvai (Northern coast)

5.5 Comparison of the Results

Wind roses for annual distribution of wind speeds in Ponnalai and Pooneryn stations are shown in Figure 5.9. Wind rose is a graphical method to illustrate the wind speed frequencies for each direction. Here the directions have been divided in to 12 intervals with 300 per each.

Figure 5.9: Wind roses to illustrate annul distribution of wind for Ponnalai and Pooneryn stations

As it can be clearly seen in Figure 5.9 the major wind activities that domain in Jaffna Peninsula region are North-Eastern and South-Western monsoons. The monthly variations in wind roses for these regions are shown in Appendix C.

The generated map indicated the regions with higher wind energy potential by showing a similar pattern to the existing wind resource maps. However, the generated map contains smaller wind power densities than the anticipated values from the most of the regions in the existing maps. Figure 5.10 shows a visual comparison between the generated map with the existing wind resource maps for Sri Lanka. A summary of the maps shown in Figure 5.10 (a)-(e) is indicated in Table 5.1.

Wind Power Distribution Map for

Jaffna Peninsula Region

Wind power density at 50 m height in Wm-2 > 500 470 - 500 440 - 470 410 - 440 360 - 410 300 - 360 200 - 300 < 200 (a) Wind power distribution map (b) Wind resource map developed by proposed in this study NREL

(e) Wind resource map developed by DMC

Figure 5.10: Comparison between the proposed and existing wind resource maps for Sri Lanka (Original in Colour)

Table 5.1: Detailed comparison between the proposed and existing wind resource maps for Sri Lanka (Original in Colour) Map Developer Resolution Wind Power Densities in the No. Selected Region Minimum Maximum (a) Proposed in this study 150 m x < 200 Wm-2 > 500 Wm-2 150 m (b) NERL 400 m x 0-200 Wm-2 500-600 Wm-2 400 m (c) AWS Truepower, 200 m x 5.50-5.75 ms-1 8.00-8.25 ms-1 LLC 200 m (d) VORTEX 3000 m x 7.0 ms-1 8.0 ms-1 3000 m (e) DMC 10000 m x 16-20 Knots 30-37 Knots 10000 m

5.6 Summary of the Chapter

Jaffna Peninsula region of Sri Lanka was selected as a suitable region for the analysis by considering its the wind data availability, its higher wind energy potential and power grid availability of that region.

WAsP is a commercially available CFD based software package which has been used to analyze wind data for more than 20 years in many different parts of the world. To obtain accurate results from WAsP the wind data should be extrapolated within a range of 50 km for a flat terrain. Hence, the map numbers 1 (Manipay), 2 (Point Pedro), 3 (Jaffna), 4 (Chavakachcheri) and 8 (Kilinochchi) obtained from the Survey Department of Sri Lanka were selected for the analysis to be used with the data available in Ponnalai and Pooneryn stations.

By careful inspection the blunders in the wind data were removed manually before starting the analysis. The 10-minute average wind speeds of the topmost two anemometers were averaged and used for the analysis, because those are the least disturbed anemometers at the wind mast.

The sequence of feeding the data to WAsP is mentioned below.

Three known coordinates were fed to the software using the Kandawala coordinate system. The World Coordinate Converter (online) was used for this coordinate conversion between GPS and Kandawala coordinate systems.

The surface roughness values were defined using the guidelines provided in the literature. The roughness change lines were assigned with two values for its both sides. Orography of the terrain was defined by demarcating the contour lines.

The effect from the sheltering obstacles was neglected since the considered height for the study is more than 40 m from the ground level. Generally, this effect will be significant when the considered height is less than three times the height of the obstacle. In Jaffna region most of the surface obstacles are not lying within that influenced zone.

Wind power distribution map for Jaffna Peninsula region were generated to illustrate the annual variations of the wind. The generated map has been modified using MATLAB software to obtain a map with high resolutions.

Coastal regions such as Veravil, Pooneryn, Ampan, Punkudutivu, Kayts, Kankesanturai, Ponnalli Khadu, Karainagar, Mandaitivu and Alvai were identified as the regions which have the highest wind energy potential in Jaffna Peninsula.

6. CONCLUSIONS AND RECOMMENDATIONS

6.1 Conclusions

As per one of the objective of this study, using the wind data obtained from 24 weather stations a wind loading map for Sri Lanka was generated for different return periods (5, 10, 50, 100, 200, 500 and 1000 years) and for different averaging time periods (3- second gust, 10-minute average and hourly mean). The data used were the monthly maximums of 3-minute average and instantaneous maximum wind speeds, recorded by the Department of Meteorology Sri Lanka over a period of about 35 years. Extreme value distributions called Gringorten and Gumbel methods were tested to predict the extreme wind speeds. Finally, the Gringorten methods was adopted due to its unbiased nature. Surfer software and Kriging interpolation technique were used to interpolate the values between the weather stations.

The generated wind contours for both 3-second gust and 10-minute average basic wind speeds were analyzed for defining the wind loading zones for Sri Lanka. Finally, some minor modifications have been added to these zone margins to avoid the unrealistic values occurred in some regions due to the lack of wind data.

The map generated has a pattern similar to the pattern in the map that is in use at present. However, the basic wind speeds stated in the proposed map are slightly higher than the values stated in the existing map. The regions such as Ratnapura and coastal regions in Mannar, Hambantota, Galle and Matara districts also show much higher wind speeds compared to the wind speeds that are given in the previous wind loading map.

The other objective of this study was to develop a micro scale wind power distribution map for a selected region in Sri Lanka and identify the potential regions to establish wind power plants. The Jaffna Peninsula region was selected as it was previously identified as a potential region for the wind power generation. The 10-minute average

wind speeds obtained from SLSEA and 1:50000 scaled survey maps obtained from Survey Department of Sri Lanka were used to develop this map. A CFD based model was used to generate the map. The resolution of the map was increased up to 150 m x 150 m (5” x 5”) by spline fit interpolation technique. The obtained map shows a similar pattern shown in previously generated maps.

6.2 Recommendations

The wind loading map (basic wind speed map) generated in this study is recommended to use with any code of practice related to wind actions for the structures in Sri Lanka. Therefore, the map shown in Figure 4.9 and the values shown in Table 4.6 are recommended to use with following codes of practice with different averaging time periods.

 CP 3: Chapter V-2 (3-Second gust)  BS 6399-2 (Hourly mean)  EN 1991-1-4 (10-Minutes average)  AS/NZS 1170.2 (3-Second gust)

When these values are used with Eurocode 1, the altitude factor should be used as 1.0 since terrain altitude has already been incorporated into the analysis.

The regions which have an average wind power density more than 500 Wm-2 are listed below. The Islands which have not been connected by the road network such as Nainativu, Analaitivu and Eluvaitivu were excluded from the list.

 Kankesanturai (Northern coast)  Ponnalli Khadu (Western coast)  Karainagar (North-Western coast of the Island)  Mandaitivu (Southern coast of the Island)  Alvai (Northern coast)

Above regions are recommended to establish wind power plants depending on the availability of the land, transmission grid and the transportation.

REFERENCE LIST

[1] C. S. Lewangamage, and M. T. R. Jayasinghe, “Report on recent development of wind code in Sri Lanka,” presented at 7th Workshop on Regional Harmonization of Wind Loading and Wind Environmental Specifications in Asia-Pacific Economies (APEC-WW 2012), Vietnam, 2012.

[2] Ministry of Local Government - Housing and Construction, Design of Buildings for Higher Winds - Sri Lanka, Colombo: pp. 7/9-7/21, 1980.

[3] Disaster Management Centre, dmc.gov.lk.. “Hazard Profiles of Sri Lanka,” Disaster Management Centre, 2018. [Online] Available: http://www.dmc.gov.lk/images/hazard/hazard/Tropical_Cyclones.html. [Accessed Feb. 13, 2018].

[4] British Standards Institution, Code of Basic Data for the Design of Buildings - Chapter V: Loading - Part 2: Wind Loads, CP 3: Chapter V-2:1972.

[5] British Standards Institution, Loading for Building - Part 2: Code of Practice for Wind Loads, BS 6399-2:1997.

[6] European Committee for Standardization, Eurocode 1: Actions on Structures – Part 1-4: General actions - Wind Actions, EN 1991-1-4:2005+A1.

[7] Standards Australia, Structural Design Actions – Part 2: Wind Actions, AS/NZS 1170.2:2002.

[8] M. Narayana, “Validation of Wind Resource Assessment Model (WRAM) map of Sri Lanka, using measured data, and evaluation of wind power generation potential in the country,” Energy for Sustainable Development, vol. 12, no. 1, Mar., pp. 64-68, 2008.

[9] K. D. W. Nandalal, and H. Abeyruwan, “An attempt towards wind zone demarcation for Sri Lanka,” presented at 6th Workshop on Regional Harmonization of Wind Loading and Wind Environmental Specifications in Asia-Pacific Economies (APEC-WW 2010), South Korea, 2010.

[10] A. S. Alnuaimi, M. A. Mohsinb, and K. H. Al-Riyamic, “A basic wind speed map for Oman,” the Journal of Engineering Research, vol. 11, no. 2, pp. 64-78, 2014.

[11] N. Lakshmanan, S. Gomathinayagam, P. Harikrishna, A. Abraham, and S. C. Ganapathi, “Basic wind speed map of India with long-term hourly wind data,” Currant Science, vol. 96, no. 7, pp. 911-922, 2009.

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[12] M. Kasperski, “A new wind zone map of Germany,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 90, pp. 1271–1287, 2002.

[13] S. A. Hsu, “Estimating 3-second and maximum instantaneous gusts from 1- minute sustained wind speeds during a hurricane,” Electronic Journal of Structural Engineering, pp. 77-79, 2008.

[14] H. A. Panofsky, and J. A. Dutton, Atmospheric Turbulence: Models and Methods for Engineering Applications, Wiley, 1984.

[15] G. J. McRae, W. R. Goodin, and J. H. Seinfeld, “Mathematical modelling of photochemical air pollution,” Environmental Quality Laboratory, Pasadena, California, USA, EQL Report No. 18, 1982.

[16] B. A. Harper, J. D. Kepert, and J. D. Ginger, Guidelines for Converting between Various Wind Averaging Periods in Tropical Cyclone Conditions, World Meteorological Organization, Oct. 2008, pp. 2-5.

[17] A. F. Jenkinson, “The frequency distribution of the annual maximum (or minimum) of meteorological elements,” Quarterly Journal of the Royal Meteorological Society, vol. 81, pp. 158-171, 1955.

[18] J. D. Holmes, Wind loading of structures. 5th ed., New York: Taylor and Francis, 2007, pp. 29-49.

[19] E. J. Gumbel, Statistics of Extremes. New York, USA: Colombia University Press, 1958.

[20] I. Gringorten, “A plotting rule for extreme probability paper,” Journal of Geophysical Research, vol. 68, no. 3, pp. 813-814, 1963.

[21] D. Elliot, M Schwartz, G. Scott, S. Haymes, D. Heimiller, and R. George, “Wind energy resource atlas of Sri Lanka and the Maldives,” National Renewable Energy Laboratory, USA, Tech. Report. 500-34518, Aug. 2003.

[22] AWS Truepower, “Wind resource of Sri Lanka,” 2013. [Online]. Available: https://www.awstruepower.com/knowledge-center/maps/. [Accessed: Aug. 12, 2016].

[23] Vortex FdC, “Sri Lanka wind resource map,” 2014. [Online]. Available: http://www.vortexfdc.com/sri-lanka-wind-map. [Accessed: Aug. 15, 2016].

[24] T. A. Wickramasinghe and M. Narayana, “Solar and wind resource assessment in Sri Lanka,” National Engineering Research & Development Centre of Sri Lanka, Ja-Ela, Sri Lanka, 2014.

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[25] N. G. Mortensen, D. N. Heathfield, L. Myllerup, L. Landberg, and O. Rathmann, “Getting started with WAsP 9,” Risø National Laboratory, Technical University of Denmark, Roskilde, Denmark, Tech. Report. Risø-I- 2571(EN), Jun. 2007.

[26] S. Al-deen, A. Yamaguchi, and T. Ishihara, “A physical approach to wind speed prediction for wind energy forecasting,” In Proc. 4th International Symposium on Computational Wind Engineering (CWE2006), Yokohama, 2006, pp. 349- 352.

[27] M. Brower, J. W. Zack, B. Bailey, M. N. Schwartz, and D. L. Elliott, “Mesoscale modeling as a tool for wind resource assessment and mapping,” TrueWind Solutions, Albany, New York, and National Renewable Energy Laboratory, Golden, Colorado, 2004.

[28] A. Z. Dhunny, M. R. Lollchund, and S. D. D. V. Rughooputh, “Wind energy evaluation for a highly complex terrain using Computational Fluid Dynamics (CFD),” Renewable Energy, vol. 101, pp. 1-9, 2017.

[29] H. Lettau, and K. Lettau, “Shortwave radiation climatonomy,” Tellus, vol. 21, no. 2, pp. 208-222, 1969.

[30] N. G. Mortensen, D. N. Heathfield, O. Rathmann, and M. Nielsen, “Wind Atlas Analysis and Application Program: WAsP 10 help facility,” Department of Wind Energy, Technical University of Denmark, Roskilde, Denmark, Tech. Report, 2011.

[31] E. L. Petersen, I. Troen, S. Frandsen, and K. Hedegaard, “Wind atlas for Denmark; A rational method for wind energy siting,” Risø National Laboratory, Roskilde, Denmark, Tech. Report. Risø-R-428, pp. 229, 1981.

[32] Ministry of Power and Energy, “Wind & Solar Measuring Stations,” 2015. [Online]. Available: http://powermin.gov.lk/solar/?services=solar-measuring- stations. [Accessed: Aug. 18, 2016].

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APPENDIX – A: POSITIONING OF WEATHER MEASURING INSTRUMENTS IN THE WIND MASTS

Figure A.1: Instrument installation on Ponnalai station by SLSEA [32]

103

Figure A.2: Instrument installation on Pooneryn station by SLSEA [32]

104

Figure A.3: Instrument installation on Kokkilai station by SLSEA [32]

105

APPENDIX – B: GRAPHS GENERATED BY APPLYING THE GRINGORTEN AND GUMBEL METHODS FOR THE WEATHER STATIONS IN SRI LANKA

35 )

1 y = 2.7724x + 13.622 - 30 R² = 0.9155 25 20 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 5 6 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.1: Application of Gringorten method for Anuradhapura station

) 1 - 30 y = 2.9063x + 13.595 R² = 0.9019 25 20 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 5 -ln{-ln[m/(N+1)]}

Figure B.2: Application of Gumbel method for Anuradhapura station

106

35 y = 3.7936x + 12.42

) R² = 0.9701 1

- 30 25 20 15 y = 3.3616x + 13.726 10 R² = 0.9869

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 5 6 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.3: Application of Gringorten method for Badulla station

35 y = 4.598x + 10.832

) R² = 0.9654 1

- 30 25 20 15 y = 3.5429x + 13.682 10 R² = 0.9816

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 5 -ln{-ln[m/(N+1)]}

Figure B.4: Application of Gumbel method for Badulla station

107

35 y = 3.3474x + 16.348

) R² = 0.9766 1

- 30 25 20 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 5 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.5: Application of Gringorten method for Bandarawela station

35 y = 3.563x + 16.295

) R² = 0.9746 1

- 30 25 20 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 5 -ln{-ln[m/(N+1)]}

Figure B.6: Application of Gumbel method for Bandarawela station

108

45 y = 5.2735x + 22.545

) 40 R² = 0.9242

1 - 35 30 25 20 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.7: Application of Gringorten method for Batticaloa station

45 y = 6.1688x + 22.308

) 40

1 R² = 0.9462 - 35 30 25 20 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 -ln{-ln[m/(N+1)]}

Figure B.8: Application of Gumbel method for Batticaloa station

109

30 y = 3.6535x + 9.4241

) R² = 0.9745 1 - 25

5 Ranked Wind Speed (ms Speed Wind Ranked

0 -2 0 2 4 6 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.9: Application of Gringorten method for Colombo station

30 y = 3.8206x + 9.3812

) R² = 0.9735 1 - 25

5 Ranked Wind Speed (ms Speed Wind Ranked

0 -2 -1 0 1 2 3 4 5 -ln{-ln[m/(N+1)]}

Figure B.10: Application of Gumbel method for Colombo station

110

) y = 2.3305x + 12.739 1 - 25 R² = 0.9706 20

15 y = 3.765x + 6.8051 R² = 0.9887 10

5 Ranked Wind Speed (ms Speed Wind Ranked

0 -2 -1 0 1 2 3 4 5 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.11: Application of Gringorten method for Deniyaya station

) 1 - 25 y = 2.467x + 12.706 R² = 0.9666 20 y = 5.1963x + 3.0929 15 R² = 0.9761 10

5 Ranked Wind Speed (ms Speed Wind Ranked

0 -2 -1 0 1 2 3 4 5 -ln{-ln[m/(N+1)]}

Figure B.12: Application of Gumbel method for Deniyaya station

111

30 y = 2.6062x + 19.528

) R² = 0.8605 1 - 25

5 Ranked Wind Speed (ms Speed Wind Ranked

0 -2 -1 0 1 2 3 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.13: Application of Gringorten method for Galle station

30 y = 3.1472x + 19.403 )

1 R² = 0.8312 - 25

5 Ranked Wind Speed (ms Speed Wind Ranked

0 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 -ln{-ln[m/(N+1)]}

Figure B.14: Application of Gumbel method for Galle station

112

y = 3.6754x + 17.809 )

1 35 R² = 0.9806 - 30 25 20 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 5 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.15: Application of Gringorten method for Hambantota station

y = 3.9295x + 17.747 )

1 35 - R² = 0.9786 30 25 20 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 5 -ln{-ln[m/(N+1)]}

Figure B.16: Application of Gumbel method for Hambantota station

113

18 y = 0.8811x + 13.336

) 16

1 R² = 0.9364 - 14 12 10 8 6 4

Ranked Wind Speed (ms Speed Wind Ranked 2 0 -2 -1 0 1 2 3 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.17: Application of Gringorten method for Jaffna station

18 y = 1.1113x + 13.275

) 16

1 R² = 0.9518 - 14 12 10 8 6 4

Ranked Wind Speed (ms Speed Wind Ranked 2 0 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 -ln{-ln[m/(N+1)]}

Figure B.18: Application of Gumbel method for Jaffna station

114

30 y = 2.7157x + 14.206

) R² = 0.9432 1 - 25

20 y = 1.6446x + 16.582 R² = 0.961 15

5 Ranked Wind Speed (ms Speed Wind Ranked

0 -2 0 2 4 6 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.19: Application of Gringorten method for Katugastota station

30 y = 2.8729x + 14.162

) R² = 0.9522 1 - 25

20 y = 1.8631x + 16.264 R² = 0.9622 15

5 Ranked Wind Speed (ms Speed Wind Ranked

0 -2 -1 0 1 2 3 4 5 -ln{-ln[m/(N+1)]}

Figure B.20: Application of Gumbel method for Katugastota station

115

40 y = 5.4949x + 14.855

) R² = 0.8825

1 35 - 30 y = 1.1119x + 26.067 25 R² = 0.8042 20 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 5 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.21: Application of Gringorten method for Katunayake station

40 y = 6.0237x + 14.703

) R² = 0.9045

1 35 - 30 25 y = 1.4349x + 25.494 R² = 0.8357 20 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 -ln{-ln[m/(N+1)]}

Figure B.22: Application of Gumbel method for Katunayake station

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35 y = 3.2955x + 15.786

) R² = 0.9311 1

- 30 25 y = 2.108x + 20.166 R² = 0.9784 20 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 5 6 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.23: Application of Gringorten method for Kurunegala station

35 y = 3.4791x + 15.739

) R² = 0.9314 1

- 30 25 y = 2.8347x + 18.283 20 R² = 0.9903 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 5 -ln{-ln[m/(N+1)]}

Figure B.24: Application of Gumbel method for Kurunegala station

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45 y = 5.6344x + 16.667

) 40 R² = 0.9794

1 - 35 30 y = 1.268x + 32.828 25 R² = 0.9978 20 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 5 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.25: Application of Gringorten method for Maha Iluppallama station

45 y = 6.0092x + 16.568

) 40 R² = 0.9843

1 - 35 y = 1.7545x + 31.597 30 R² = 0.991 25 20 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 5 -ln{-ln[m/(N+1)]}

Figure B.26: Application of Gumbel method for Maha Iluppallama station

118

40 y = 3.6454x + 23.385

) R² = 0.9614

1 35 - 30 y = 2.4003x + 26.345 25 R² = 0.8561 20 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.27: Application of Gringorten method for Mannar station

40 y = 4.1023x + 23.267

) R² = 0.9725

1 35 - 30 y = 3.0852x + 25.5 R² = 0.8937 25 20 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 -ln{-ln[m/(N+1)]}

Figure B.28: Application of Gumbel method for Mannar station

119

25 y = 2.4446x + 11.465

) R² = 0.9668 1 - 20 y = 2.9148x + 9.6063 15 R² = 0.9724

5 Ranked Wind Speed (ms Speed Wind Ranked

0 -2 -1 0 1 2 3 4 5 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.29: Application of Gringorten method for Monaragala station

25 y = 2.5981x + 11.428

) R² = 0.9634 1 - 20

y = 3.5908x + 8.3038 15 R² = 0.9492

5 Ranked Wind Speed (ms Speed Wind Ranked

0 -2 -1 0 1 2 3 4 5 -ln{-ln[m/(N+1)]}

Figure B.30: Application of Gumbel method for Monaragala station

120

50 y = 4.436x + 22.134

) 45 R² = 0.9621 1 - 40 35 y = 1.7399x + 29.349 30 R² = 0.958 25 20 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 5 6 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.31: Application of Gringorten method for Nuwara Eliya station

45 y = 4.7151x + 22.054

) 40 R² = 0.9744

1 - 35 y = 2.2342x + 28.2 30 R² = 0.9782 25 20 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 5 -ln{-ln[m/(N+1)]}

Figure B.32: Application of Gumbel method for Nuwara Eliya station

121

25 y = 1.9286x + 15.352

) R² = 0.809 1 - 20

5 Ranked Wind Speed (ms Speed Wind Ranked

0 -2 -1 0 1 2 3 4 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.33: Application of Gringorten method for Polonnaruwa station

25 y = 2.166x + 15.29

) R² = 0.8204 1 - 20

5 Ranked Wind Speed (ms Speed Wind Ranked

0 -2 -1 0 1 2 3 4 -ln{-ln[m/(N+1)]}

Figure B.34: Application of Gumbel method for Polonnaruwa station

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) y = 2.788x + 12.572 1 - 25 R² = 0.9797

5 Ranked Wind Speed (ms Speed Wind Ranked

0 -2 -1 0 1 2 3 4 5 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.35: Application of Gringorten method for Pottuvil station

25 y = 3.0295x + 12.512

) R² = 0.9807 1 - 20

5 Ranked Wind Speed (ms Speed Wind Ranked

0 -2 -1 0 1 2 3 4 -ln{-ln[m/(N+1)]}

Figure B.36: Application of Gumbel method for Pottuvil station

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40 )

1 35 -

30 y = 2.6183x + 12.148 25 R² = 0.7798 20 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 5 6 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.37: Application of Gringorten method for Puttalam station

40 )

1 35 - 30 y = 2.7057x + 12.143 25 R² = 0.7465 20 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 5 -ln{-ln[m/(N+1)]}

Figure B.38: Application of Gumbel method for Puttalam station

124

35 )

1 y = 2.5459x + 15.079

- 30 R² = 0.9854 25 20 y = 3.1497x + 13.485 R² = 0.9739 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 5 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.39: Application of Gringorten method for Rathmalana station

) 1 - 30 y = 2.6814x + 15.049 R² = 0.9753 25 20 y = 3.6774x + 12.609 R² = 0.9523 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 4 5 -ln{-ln[m/(N+1)]}

Figure B.40: Application of Gumbel method for Rathmalana station

125

y = 6.4527x + 22.703

) 1 - 50 R² = 0.8287

40 y = 3.0746x + 29.963 30 R² = 0.8358

10 Ranked Wind Speed (ms Speed Wind Ranked

0 -2 -1 0 1 2 3 4 5 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.41: Application of Gringorten method for Ratnapura station

y = 7.01x + 22.547

) 1 - 50 R² = 0.8429

40 y = 4.1949x + 27.552 30 R² = 0.8754

10 Ranked Wind Speed (ms Speed Wind Ranked

0 -2 -1 0 1 2 3 4 -ln{-ln[m/(N+1)]}

Figure B.42: Application of Gumbel method for Ratnapura station

126

) y = 2.7832x + 11.92 1 - 25 R² = 0.9823

20 y = 3.4582x + 9.7665 15 R² = 0.9512

5 Ranked Wind Speed (ms Speed Wind Ranked

0 -2 -1 0 1 2 3 4 5 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.43: Application of Gringorten method for Tawalama station

) y = 2.7832x + 11.92 1 - 25 R² = 0.9823

20 y = 3.4582x + 9.7665 15 R² = 0.9512

5 Ranked Wind Speed (ms Speed Wind Ranked

0 -2 -1 0 1 2 3 4 5 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.44: Application of Gumbel method for Tawalama station

127

y = 4.7627x + 22.749 )

1 35 R² = 0.8922 - 30 25 20 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -2 -1 0 1 2 3 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.45: Application of Gringorten method for Trincomalee station

y = 5.8829x + 22.476 )

1 35

- R² = 0.8698 30 25 20 15 10

Ranked Wind Speed (ms Speed Wind Ranked 5 0 -1 -1 0 1 1 2 2 -ln{-ln[m/(N+1)]}

Figure B.46: Application of Gumbel method for Trincomalee station

128

y = 2.6847x + 10.654 )

1 R² = 0.9688 - 20 y = 2.9571x + 9.4338 15 R² = 0.9638

5 Ranked Wind Speed (ms Speed Wind Ranked

0 -2 -1 0 1 2 3 4 5 -ln{-ln[(m-0.44)/(N+0.12)]}

Figure B.47: Application of Gringorten method for Vavuniya station

y = 2.9235x + 10.596

) 1

- R² = 0.9675 20

15 y = 3.7725x + 8.0717 R² = 0.9382 10

5 Ranked Wind Speed (ms Speed Wind Ranked

0 -2 -1 0 1 2 3 4 -ln{-ln[m/(N+1)]}

Figure B.48: Application of Gumbel method for Vavuniya station

129

APPENDIX – C: GRAPHS GENERATED TO VISUALIZE THE MONTHLY VARIATIONS IN WIND FOR NORTHERN REGION IN SRI LANKA

January February March April

May June July August

September October November December

Figure C.1: Wind roses for Ponnalai station for each month of the year

130

January February March April

May June July August

September October November December

Figure C.2: Wind roses for Pooneryn station for each month of the year

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VITA

Name: Weihena Liyanage Sanjaya Maduranga

Date of Birth: October 29, 1990

Nationality; Sri Lankan

Address: 290/3, Pragathi Place, Sudarshana Mawatha, Malabe.

Email: [email protected]

Education:

2016 –2019 M.Phil., Department of Civil Engineering, University of Moratuwa, Sri Lanka. Thesis: An assessment of wind loading and wind energy potential for Sri Lanka Supervisor: Dr. C. S. Lewangamage

2010 –2015 B.Sc. Eng. (Hons.), Department of Civil Engineering, University of Moratuwa, Sri Lanka. Thesis: Rainwater harvesting quality and quantity issues in Sri Lanka Supervisor: Prof. (Mrs.) N. Rathnayake

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Working Experience:

08/2018 – Present Lecturer (Probationary), Division of Civil Engineering Technology, Institute of Technology, University of Moratuwa, Sri Lanka.

04/2016 – 08/2018 Research Assistant, Department of Civil Engineering, University of Moratuwa, Sri Lanka.

05/2015 – 03/2016 Lecturer, Department of Civil Engineering, CINEC Maritimes Campus, Malabe, Sri Lanka.

Publications: W. L. S. Maduranga, and C. S. Lewangamage, “Development of wind loading maps for Sri Lanka for use with different wind loading codes,” Engineer, vol. 51, no. 3, pp. 41-49, 2018.

W. L. S. Maduranga, and C. S. Lewangamage, “A statistical based approach for demarcating a wind loading map for Sri Lanka,” In Proc. Annual Sessions - 2017 of Society of Structural Engineers, Sri Lanka, 2017, pp. 18-24.

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