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
  • [41] A Note on the Prior Distributions of Weibull Parameters for the Reliability Function
    Moala, Fernando Antonio
    Rodrigues, Josemar
    Tomazella, Vera Lucia D.
    COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2009, 38 (07) : 1041 - 1054
  • [42] Estimation of wind power potential in a region of Goa, India using weibull distribution function
    Balakrishna Moorthy, C. (cbkmoorthy@gmail.com), 1600, Begell House Inc. (15): : 2 - 4
  • [43] Assessment of wind energy potentiality at Kudat and Labuan, Malaysia using Weibull distribution function
    Islam, M. R.
    Saidur, R.
    Rahim, N. A.
    ENERGY, 2011, 36 (02) : 985 - 992
  • [44] Estimation of diameter distributions for Pinus patula with the Weibull Function
    Perez-Lopez, Eloisa
    Santiago-Garcia, Wenceslao
    Quinonez-Barraza, Geronimo
    Rodriguez-Ortiz, Gerardo
    Santiago-Garcia, Elias
    Ruiz-Aquino, Faustino
    MADERA Y BOSQUES, 2019, 25 (03):
  • [45] Wind speed analysis using Weibull and lower upper truncated Weibull distribution in Bangladesh
    Jahan, Saima
    Masseran, Nurulkamal
    Zin, W. Z. Wan
    ENERGY REPORTS, 2024, 11 : 5456 - 5465
  • [46] Wind speed estimation based on a novel multivariate Weibull distribution
    Salim, Omar M.
    Dorrah, Hassen Taher
    Hassan, Mahmoud Adel
    IET RENEWABLE POWER GENERATION, 2019, 13 (15) : 2762 - 2773
  • [47] Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis
    Seguro, JV
    Lambert, TW
    JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS, 2000, 85 (01) : 75 - 84
  • [48] Analysis of the upper-truncated Weibull distribution for wind speed
    Kantar, Yeliz Mert
    Usta, Ilhan
    ENERGY CONVERSION AND MANAGEMENT, 2015, 96 : 81 - 88
  • [49] A Theoretical Analysis on Parameter Estimation for the Weibull Wind Speed Distribution
    Tuzuner, Akiner
    Yu, Zuwei
    2008 IEEE POWER & ENERGY SOCIETY GENERAL MEETING, VOLS 1-11, 2008, : 505 - 510
  • [50] Modeling Wind-Speed Statistics beyond the Weibull Distribution
    Lencastre, Pedro
    Yazidi, Anis
    Lind, Pedro G.
    ENERGIES, 2024, 17 (11)