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Estimation of different wind characteristics parameters and accurate wind resource assessment for Kadavu, Fiji

1 School of Engineering, The University of the South Pacific, Laucala Campus, Laucala Bay Road, Suva, Fiji
2 School of Computing, Information and Mathematical Sciences, The University of the South Pacific, Laucala Campus, Laucala Bay Road, Suva, Fiji

Topical Section: Wind Energy

Wind resource assessment is carried out for a site in Kadavu, Fiji Islands. This included estimating the Weibull parameters and wind power density using ten different methods and carrying out an economic analysis. The wind speeds at 34 m and 20 m above ground level, wind direction, atmospheric pressure and temperature were measured for 18 months and statistically analyzed. The overall average wind speed at a height of 34 m above ground level was found to be 3.63 m/s. The seasonal averages for the site were 3.81 m/s and 3.40 m/s for summer and winter respectively. The diurnal variation of the wind shear for the site was correlated with the temperature variation. The Moments Method was found to be the best method for the entire period of study. The Modified Maximum Likelihood Method was found to be the best for the summer season whilst Median and Quartile method was the best for the winter period. The mean wind power density at the location was found to be 45.88 W/m2. The WAsP software was used to create the wind resource map. Five potential sites were selected installing the wind turbines and for carrying out the economic analysis, which included an estimation of annual energy production. It was found that, for the five turbines average capacity factor would be 20.05%. The payback period for installing the wind turbines at 50 m above the ground level is estimated to be between 6.99 and 8.74 years.
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References

1. United Nations Department of Economic and Social Affairs (2009) A global green new deal for climate, energy, and development. Technical note. Available from: http://sustainabledevelopment.un.org/content/documents/cc_global_green_new_deal.pdf.

2. Shahbaz M, Loganathan N, Zeshan M, et al. (2015) Does renewable energy consumption add in economic growth? An application of auto-regressive distributed lag model in Pakistan. Renewable Sustainable Energy Rev 44: 576-585.

3. Alrikabi NKMA (2014) Renewable energy types. J Clean Energy Technol 2: 61-64.

4. Shahzad U (2012) The need For renewable energy sources. Inf Technol Electr Eng J 4: 16-19.

5. Panwar NL, Kaushik SC, Kothari S (2011) Role of renewable energy sources in environmental protection: A review. Renewable Sustainable Energy Rev 15: 1513-1524.    

6. Razykov TM, Ferekides CS, Morel D, et al. (2011) Solar photovoltaic electricity: Current status and future prospects. Sol Energy 85: 1580-1608.    

7. Sholler D (2011) Wind power: Harnessing history to meet the energy demand. Penn McNair Res J 3.

8. Shu ZR, Li QS, Chan PW (2015) Statistical analysis of wind characteristics and wind energy potential in Hong Kong. Energy Convers Manage 101: 644-657.    

9. Dabbaghiyan A, Fazelpour F, Abnavi MD, et al. (2015) Evaluation of wind energy potential in province of Bushehr, Iran. Renewable Sustainable Energy Rev 55: 455-466.

10. Fazelpour F, Markarian E, Soltani N (2017) Wind energy potential and economic assessment of four locations in Sistan and Balouchestan province in Iran. Renewable Energy 109: 646-667.    

11. Soulouknga MH, Doka SY, Revanna N, et al. (2018) Analysis of wind speed data and wind energy potential in Faya-Largeau, Chad, using Weibull distribution. Renewable Energy 121: 1-8.    

12. Bassyouni M, Gutub SA, Javaid U, et al. (2015) Assessment and analysis of wind power resource using weibull parameters. Energy Explor Exploit 33: 105-122.    

13. Mohammadi K, Alavi O, Mostafaeipour A, et al. (2016) Assessing different parameters estimation methods of Weibull distribution to compute wind power density. Energy Convers Manage 108: 322-335.    

14. Werapun W, Tirawanichakul Y, Waewsak J (2015) Comparative study of five methods to estimate Weibull parameters for wind speed on Phangan Island, Thailand. Energy Procedia 79: 976-981.    

15. Azad AK, Rasul M, Alam M, et al. (2014) Analysis of wind energy conversion system using Weibull distribution. Procedia Eng 90: 725-732.    

16. Shoaib M, Siddiqui I, Rehman S, et al. (2019) Assessment of wind energy potential using wind energy conversion system. J Cleaner Prod 216: 346-360.    

17. Kombe EY, Muguthu J (2019) Wind energy potential assessment of Great Cumbrae Island using weibull distribution function. J Energy Res Rev: 1-8.

18. Fiji Bureau of Statistics (2017) 2017 Population and Housing Census. Available from: http://www.statsfiji.gov.fj/index.php/statistics/population-censuses-and-surveys.

19. The Editors of Encyclopædia Britannica. Kadavu Island. Available from: https://www.britannica.com/place/Kadavu-Island.

20. Kadavu Map. Available from: http://nztourmaps.com/fiji_map_kadavu.htm.

21. Aukitino T, Khan M, Ahmed MR (2017) Wind energy resource assessment for Kiribati with a comparison of different methods of determining Weibull parameters. Energy Convers Manage 151: 641-660.    

22. Zhang MH (2015) Wind Resource Assesement and Micrositting : Science and Engineering. Singapore: John Wiley & Sons Singapore Pte. Ltd.

23. Fırtın E, Güler Ö, Akdağ SA (2011) Investigation of wind shear coefficients and their effect on electrical energy generation. Appl Energy 88: 4097-4105.    

24. Gualtieri G, Secci S (2011) Wind shear coefficients, roughness length and energy yield over coastal locations in Southern Italy. Renewable Energy 36: 1081-1094.    

25. Rehman S, Al-Abbadi NM (2008) Wind shear coefficient, turbulence intensity and wind power potential assessment for Dhulom, Saudi Arabia. Renewable Energy 33: 2653-2660.    

26. Singh K, Bule L, Khan M, et al. (2019) Wind energy resource assessment for Vanuatu with accurate estimation of Weibull parameters. Energy Explor Exploit 37: 1804-1832.    

27. Justus CG, Hargraves WR, Mikhail A, et al. (1978) Methods for estimating wind speed distributions. J Appl Meteorolgy 17: 350-353.    

28. Chaurasiya PK, Ahmed S, Warudkar V (2017) Study of different parameters estimation methods of Weibull distribution to determine wind power density using ground based Doppler SODAR instrument. Alexandria Eng J 55: 2299-2311.

29. Chaurasiya PK, Ahmed S, Warudkar V (2018) Comparative analysis of Weibull parameters for wind data measured from met-mast and remote sensing techniques. Renewable Energy 115: 1153-1165.    

30. Rocha PAC, de Sousa RC, de Andrade CF, et al. (2012) Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil. Appl Energy 89: 395-400.    

31. Lysen E (1982) Introduction to wind energy: basic and advanced Introduction to wind energy with emphasis on water pumping windmills. Amersfoort: Consultancy services wind energy developing countries (CWD).

32. Ahmad S, Hussin W, Bawadi M, et al. (2003) Analysis of wind speed variations and estimation of Weibull parameters for wind power generation in Malaysia. 2nd Dubrovnik Conference on Sustainable Development of Energy, Water and Environment Systems, Dubrovnik, Croatia, 15-20 June, 61.

33. Katinas V, Marciukaitis M, Gecevicius G, et al. (2017) Statistical analysis of wind characteristics based on Weibull methods for estimation of power generation in Lithuania. Renewable Energy 113: 190-201.    

34. Usta I, Arik I, Yenilmez I, et al. (2018) A new estimation approach based on moments for estimating Weibull parameters in wind power applications. Energy Convers Manage 164: 570-578.    

35. Arslan T, Bulut YM, Yavuz AA (2014) Comparative study of numerical methods for determining Weibull parameters for wind energy potential. Renewable Sustainable Energy Rev 40: 820-825.    

36. Szafron C (2011) Wind energy conversion systems grid connection. Master's Thesis, Wroclaw University of Technology.

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