Study on Wind Characteristics Using Bimodal Mixture Weibull Distribution for Three Wind Sites in Taiwan

Some wind speed distributions in Taiwan have been found deviating from conventional Weibull distribution. In this paper the mixture Weibull distribution was adopted to analyze the wind data observed at three wind sites having different climatic environments. The Kolmogorov-Smirnov test and wind potential energy were considered as indicators to show how the mixture Weibull function characterizes wind speed distribution. Relevant mathematical expressions are derived originally for wind energy assessment. The results show that the mixture Weibull function performs quite better than a conventional Weibull function particularly for a region where the wind speed distribution reveals two humps on it. The similar result is obtained also when wind power density is considered. The maximum errors of cumulative distribution function between observation data and mixture Weibull function are always below the critical value of 95% confidence level in Kolmogorov-Smirnov test. The relative percentage errors of wind potential energy between time-series data and theoretical values from mixture Weibull function never exceed 0.1%. It is found that the distribution pattern of wind speed would affect a lot to the electrical energy generated by an ideal turbine.

[1]  Dimitri Kececioglu,et al.  Maximum likelihood estimates, from censored data, for mixed-Weibull distributions , 1992 .

[2]  T. W. Lambert,et al.  Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis , 2000 .

[3]  Sinan Akpinar,et al.  Wind energy analysis based on maximum entropy principle (MEP)-type distribution function , 2007 .

[4]  A. Dorvlo Estimating wind speed distribution , 2002 .

[5]  E. Akpinar,et al.  Determination of the wind energy potential for Maden-Elazig, Turkey , 2004 .

[6]  O. A. Jaramillo,et al.  Wind speed analysis in La Ventosa, Mexico: a bimodal probability distribution case , 2004 .

[7]  Lin Lu,et al.  Weather data and probability analysis of hybrid photovoltaic–wind power generation systems in Hong Kong , 2003 .

[8]  V. Malačič,et al.  Weibull distribution of bora and sirocco winds in the northern Adriatic Sea , 2009 .

[9]  J. A. Carta,et al.  Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions , 2007 .

[10]  Azmi Zakaria,et al.  Wind characteristics of Oman , 2002 .

[11]  T. Chang Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application , 2011 .

[12]  Soon-Duck Kwon UNCERTAINTY ANALYSIS OF WIND ENERGY POTENTIAL ASSESSMENT , 2010 .

[13]  Brian W. Raichle,et al.  Wind resource assessment of the Southern Appalachian Ridges in the Southeastern United States , 2009 .

[14]  Seyit Ahmet Akdağ,et al.  A new method to estimate Weibull parameters for wind energy applications , 2009 .

[15]  J. A. Carta,et al.  Use of finite mixture distribution models in the analysis of wind energy in the Canarian Archipelago , 2007 .

[16]  A. Celik A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey , 2004 .

[17]  Yeliz Mert Kantar,et al.  Use of MinMaxEnt distributions defined on basis of MaxEnt method in wind power study , 2008 .

[18]  J. A. Carta,et al.  A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands , 2009 .

[19]  Hongxing Yang,et al.  Wind power potential and characteristic analysis of the Pearl River Delta region, China , 2006 .

[20]  Figen Balo,et al.  Investigation of wind characteristics and assessment of wind-generation potentiality in Uludağ-Bursa, Turkey , 2009 .

[21]  S. Parsa,et al.  Wind power statistics and an evaluation of wind energy density , 1995 .

[22]  Tsang-Jung Chang,et al.  Assessment of wind characteristics and wind turbine characteristics in Taiwan , 2003 .

[23]  J. A. Carta,et al.  A continuous bivariate model for wind power density and wind turbine energy output estimations , 2007 .

[24]  Fawzi A. L. Jowder,et al.  Wind power analysis and site matching of wind turbine generators in Kingdom of Bahrain , 2009 .

[25]  Tariq Muneer,et al.  Critical evaluation of wind speed frequency distribution functions , 2010 .

[26]  A. M. Razali,et al.  Combining two Weibull distributions using a mixing parameter , 2009 .