MEP-type distribution function: a better alternative to Weibull function for wind speed distributions

被引:76
|
作者
Li, MS [1 ]
Li, XG [1 ]
机构
[1] Univ Waterloo, Dept Mech Engn, Waterloo, ON N2L 3G1, Canada
关键词
wind speed distribution; maximum entropy principle; Weibull distribution; wind energy;
D O I
10.1016/j.renene.2004.10.003
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The probabilistic distribution of wind speed is one of the important wind characteristics for the assessment of wind energy potential and for the performance of wind energy conversion systems, as well as for the structural and environmental design and analysis. In this study, an exponential family of distribution functions has been developed for the description of the probabilistic distribution of wind speed, and comparison with the wind speed data taken from different sources and measured at different geographical locations in the world has been made. This family of distributions is developed by introducing a pre-exponential term to the theoretical distribution derived from the maximum entropy principle (MEP). The statistical analysis parameter based on the wind power density is used as the suitability judgement for the distribution functions. It is shown that the MEP-type distributions not only agree better with a variety of the measured wind speed data than the conventionally used empirical Weibull distribution, but also can represent the wind power density much more accurately. Therefore, the MEP-type distributions are more suitable for the assessment of the wind energy potential and the performance of wind energy conversion systems. (c) 2004 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1221 / 1240
页数:20
相关论文
共 50 条
  • [31] PRACTICAL APPLICATIONS OF WEIBULL DISTRIBUTION FUNCTION
    STEIGER, FH
    CHEMICAL TECHNOLOGY, 1971, (APR): : 225 - &
  • [32] A NEW MODIFIED WEIBULL DISTRIBUTION FUNCTION
    PHANI, KK
    JOURNAL OF THE AMERICAN CERAMIC SOCIETY, 1987, 70 (08) : C182 - C184
  • [33] Predictions of the solar wind speed by the probability distribution function model
    Bussy-Virat, C. D.
    Ridley, A. J.
    SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS, 2014, 12 (06): : 337 - 353
  • [34] Comparative Analysis of Wind Speed Models Using Different Weibull Distributions
    Dokur, Emrah
    Ceyhan, Salim
    Kurban, Mehmet
    ELECTRICA, 2019, 19 (01): : 22 - 28
  • [35] Estimation of monthly wind speed distribution basing on hybrid Weibull distribution
    Ihaddadene, Razika
    Ihaddadene, Nabila
    Mostefaoui, Marouane
    WORLD JOURNAL OF ENGINEERING, 2016, 13 (06) : 509 - 515
  • [36] Analysis and Comparison of Wind Potential by Estimating the Weibull Distribution Function: Application to Wind Farm in the Northern of Morocco
    Bousla, Mohamed
    Haddi, Ali
    El Mourabit, Youness
    Sadki, Ahmed
    Mouradi, Abderrahman
    El Kharrim, Abderrahman
    Mobayen, Saleh
    Zhilenkov, Anton
    Bossoufi, Badre
    SUSTAINABILITY, 2023, 15 (20)
  • [37] Comparison of Wind Energy Generation Using the Maximum Entropy Principle and the Weibull Distribution Function
    Shoaib, Muhammad
    Siddiqui, Imran
    Rehman, Shafiqur
    Rehman, Saif Ur
    Khan, Shamim
    Lashin, Aref
    ENERGIES, 2016, 9 (10)
  • [38] Evaluation of wind power potential in shelek corridor (Kazakhstan) using weibull distribution function
    Baizhuma, Zh
    Bolegenova, S. A.
    Manatbayev, R. K.
    Baktybekov, K.
    Syzdykov, A.
    INTERNATIONAL JOURNAL OF MATHEMATICS AND PHYSICS, 2018, 9 (02): : 86 - 93
  • [39] Investigation of offshore wind energy potential in Hong Kong based on Weibull distribution function
    Shu, Z. R.
    Li, Q. S.
    Chan, P. W.
    APPLIED ENERGY, 2015, 156 : 362 - 373
  • [40] ESTIMATION OF THE PARAMETERS OF THE WEIBULL FUNCTION FOR GENERATING DIAMETER DISTRIBUTIONS
    NAGEL, J
    BIGING, GS
    ALLGEMEINE FORST UND JAGDZEITUNG, 1995, 166 (9-10): : 185 - 189