[1]
Chen Z., Blaabjerg F. Wind farm: a power source in future power systems. Renewable and Sustainable Energy Reviews, Vol. 13 (2009), pp.1288-1300.
DOI: 10.1016/j.rser.2008.09.010
Google Scholar
[2]
Brano V.L., Orioli A., Ciulla G., et al. Quality of wind fitting distributions for the urban area of Palarmo, Italy. Renewable Energy, Vol. 36 (2011), pp.1026-1039.
DOI: 10.1016/j.renene.2010.09.009
Google Scholar
[3]
Peng Hu, Guo Yufeng, Wang Songyan, et al. Pattern analysis on characteristics of wind speed distribution in wind farms. Power System Technology, Vol. 34 (2010), pp.206-210.
Google Scholar
[4]
Kiss P., Jánosi I.M. Comprehensive empirical analysis of ERA-40 surface wind speed distribution over Europe. Energy Conversion and Management, Vol. 49 (2008), pp.2142-2151.
DOI: 10.1016/j.enconman.2008.02.003
Google Scholar
[5]
Azami Z., Ahmad M.R. Fitting of statistical distributions to wind speed data in Malaysia. European Journal of Scientific Research, Vol. 26 (2009), pp.6-12.
Google Scholar
[6]
Safari B., Gasore J. A statistical investigation of wind characteristics and wind energy potential based on the Weibull and Rayleigh models in Rwanda. Renewable Energy, Vol. 35 (2010), pp.2874-2880.
DOI: 10.1016/j.renene.2010.04.032
Google Scholar
[7]
Wang Miao, Zeng Lihua. Study of wind speed frequency distribution model. Journal of Hydroelectric Engineering, Vol. 30 (2011), pp.204-209.
Google Scholar
[8]
Hafzullah A.Z. Stochastic generation of hourly mean wind speed data. Renewable Energy, Vol. 29 (2004), pp.2111-2131.
DOI: 10.1016/j.renene.2004.03.011
Google Scholar
[9]
Kececioglu D.B., Wang W.D. Parameter estimation for mixed-Weibull distribution. Proceedings of Annual Reliability and Maintainability Symposium. Forum (1998), pp.247-252.
DOI: 10.1109/rams.1998.653782
Google Scholar
[10]
Carta J.A., Ramírez P. Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions. Renewable Energy, Vol. 32 (2007), pp.518-531.
DOI: 10.1016/j.renene.2006.05.005
Google Scholar
[11]
Waal D.J., Gelder P.H., Beirlant J. Joint modeling of daily maximum wind strengths through the Multivariate Burr-Gamma distribution. Journal of Wind Engineering and Industrial Aerodynamics, Vol. 92 (2004), pp.1025-1037.
DOI: 10.1016/j.jweia.2004.06.001
Google Scholar
[12]
Azami Zaharim, Siti Khadijah Najid, Ahmad Mahir Razali, et al. Analyzing Malaysian wind speed data using statistical distribution. Proceedings of the 4th IASME/WSEAS international conference on energy & environment. Forum (2009), pp.363-370.
Google Scholar
[13]
M.J.M. Stevens, P.T. Smulders. The estimation of the parameters of the Weibull wind speed distribution for wind energy utilization purposes. Wind Eng, Vol. 3 (1979), pp.132-145.
Google Scholar
[14]
A. Genc et al. Estimation of wind power potential using Weibull distribution. Energy Sources, Part A: Recov, Utiliz Environ Eff, Vol. 27 (2005), pp.809-822.
DOI: 10.1080/00908310490450647
Google Scholar
[15]
C.G. Justus, W.R. Hargraves, A. Mikhail, D. Graber. Methods for estimating wind speed frequency distributions. J Appl Meteorol, Vol. 17 (1978), pp.350-353.
DOI: 10.1175/1520-0450(1978)017<0350:mfewsf>2.0.co;2
Google Scholar