Statistical Modelling of Wind Speed Data for Mauritius

Abstract

This paper focused on the statistical modelling of wind speed data observed at two locations in Mauritius using some standard probability distribution functions (PDF). The objective was to determine the best PDF which can represent the yearly wind speed data. The PDFs considered were Weibull, Rayleigh, Lognormal, Gamma, Normal and Frechet. The parameters for each PDF were estimated from the data using the Maximum Likelihood Estimation (MLE) technique. The Chi-Square (C-S), Kolmogorov-Smirnov (K-S) and Anderson-Darling (A-D) goodness-of-fit (GOF) tests were utilized to assess the effectiveness of the PDFs. For both locations all three GOF tests revealed that the Weibull and Burr distributions fit the data when the significance level is less than 5 %.

Keywords

• Goodness-of-fit tests; Mauritius; Statistical analysis; Probability Density Functions.

Kaynakça

1. Morgan E.C., Lackner M., Vogel R.M. and Baise L.G. (2010), “Probability distributions for offshore wind speeds”. Journal of Energy Conversion and Management 52 (2011) 15–26.
2. Ahmed O., Hanane D., Roberto S., Abdelaziz M. (2010), “Monthly and seasonal assessment of wind energy characteristics at four monitored locations in Liguria region (Italy)”, Renewable and Sustainable Energy Reviews, 14, 1959-1968.
3. Elahee K. (2009), “Maurice Ile Durable Project Analysis http://www.docstoc.com/docs/99604216/Maurice-Ile- Durable-Project. Synthesis Report”.
4. Mohee R., Surroop D. and Jeeta P. (2012), “Renewable Energy Potential in Mauritius and Technology Transfer through the DIREKT Project”, Proceedings of International Conference on Agriculture, Chemical and Environmental Sciences (ICACES'2012) Oct. 6-7, 2012 Dubai (UAE).
5. Abbas K., Alamgir, Khan S.A., Ali A., Khan D.M. and Khalil U. (2012), “Statistical Analysis of Wind Speed Data in Pakistan”. World Applied Sciences Journal 18 (11): 1533-1539.
6. Azad A.K., Rasul M.G. and Yusaf T. (2014), “Statistical Diagnosis of the Best Weibull Methods for Wind Applications”. Energies 2014, 7, 3056-3085. for Agricultural
7. Zghal W., Kantchev G. and Kchau H. (2011), “Determination of recoverable wind energy for electricity generation using wind energy conversion system in Tunisia”. International Journal of Engineering, Science and Technology. 3(5), 83-92.
8. Ngala G.M., Alkali B. and Aji M.A. (2007), “Viability of wind energy as a power generation source in Maiduguri, Borno state, Nigeria”. Renewable Energy. 32(13), 2242-2246.

9. Sahin B., Bilgili M. and Akilli H. (2005), “The wind power potential of the eastern Mediterranean region of Turkey”. J Wind Eng Indust Aerodynam. 93:171–83.
10. Ucar A. and Balo F. (2009), “Investigation of wind characteristic and assessment of wind-generation potentiality in Uludag-Bursa turkey”. Applied energy 86; 333-339.
11. Keyhani A., Ghasemi-Varnamkhasti M., Khanali M. and Abbaszadeh R. (2010), “An assessment of wind energy potential as a power generation source in the capital of Iran, Tehran”. Energy.35(1); 188–201.
12. Jimenez A.R., Diazgranados J.A. and Morantes M.T.A. (2011), “Electricity generation and wind potential assessment in regions of Colombia”. Dyna 79(171); 116-122.
13. Lehri L.A, Shah S.M.A., Leghari B.A. and Tareen M.I. (2013), “Wind Potential Assessment and Optimization of Wind Turbine Blade for Coastal Area of Jiwani Balochistan Pakistan”. International Journal of Materials, Mechanics and Manufacturing. 1(2).
14. Kollu, R., Rayapudi, S.R., Narasimham, S.V.L. and Pakkurthi, K.M. (2012), “Mixture probability distribution distributions”. International Journal of Energy and Environmental Engineering. 3(27). wind speed
15. Masseran N., Razali A.M., Ibrahim K., Zaharim A. and Sopian K. (2013), “The probability distribution model of wind speed over East Malaysia”. Research Journal of Applied 6(10):1774-1779. and technology
16. AWS Scientific, Inc., (1997), “Wınd Resource Assessment Handbook Fundamentals for Conducting a Successful Monitoring Program". Prepared for National Renewable Energy Laboratory.
17. Kamran A. and Tang Y. (2012) “Comparison of Estimation Methods for Frechet Distribution with Known Shape”. Caspian Journal of Applied Sciences Research, 1(10), pp. 58-64.
18. Jagdish K.P. and Campbell B. (1996), Handbook of the Normal Distribution, Second Edition
19. Schloz F.W. (2006), Maximum Likelihood Estimation. Encyclopedia of Statistical Sciences.
20. Yilmaz V. and Celik H.E. (2008), “A Statıstıcal Approach To Estımate The Wınd Speed Dıstrıbutıon: The Case Of Gelıbolu Regıon”. Doguş University Dirges, 9 (1); 122-132.
21. Bagdonavicius V., Julius K. and Nikulin M.S. (2011), Chi-Squared Tests, in Non-parametric Tests for Complete Data, John Wiley & Sons, Inc, Hoboken, NJ, USA.
22. Banks J., Carson I.I.J.S. and Nelson B.L., (1999). Discrete-Event System Simulation. Second Edition. Prentice-Hall. New Jersey.
23. Frank J.M. (1951), “The Kolmogorov-Smirnov Test for Goodness of Fit”. Journal of the American Statistical Association. 46(253). [24] Sinclair J.C.D. and Spurr B.D. (1988), “Approximations to the Distribution Function of the Anderson—Darling Test Statistic”. Journal of the American Statistical Association 83(404).