Wind Speed and Power Density Analyses Based on Mixture Weibull and Maximum Entropy Distributions
Wind Speed and Power Density Analyses Based on Mixture Weibull and Maximum Entropy Distributions
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DOI：
10.6703/ijase.2010.8(1).39
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发表时间：
2010-10
影响因子：
--
通讯作者：
T. Chang
中科院分区：
文献类型：
--
作者：
T. Chang
风能资源是可再生能源利用的重要组成部分。为了有效地估计给定区域的风能潜力，文献中已有各种概率密度函数（pdf）。本文采用双峰混合威布尔函数（WW）和由最大熵原理（MEP）导出的概率函数，并与常规威布尔函数进行比较。在台湾三个风力发电场经历不同的气候环境测量的风速数据被选为样本数据，以测试其性能。判别标准包括Kolmogorov-Smirnov检验的最大误差、卡方误差、均方根误差和风位能的相对误差四种统计误差。结果表明，无论风速和风功率密度如何，所提出的WW和MEP概率密度函数都比传统的Weibull概率密度函数更好地描述了风场特征，特别是对于风场呈现双峰的位置，对于风速分布，WW概率密度函数根据Kolmogorov-Smirnov检验描述得最好，而对于风功率密度，MEP概率密度函数优于其他函数。
Wind resource is important part of the utilization of renewable energy. To effectively estimate the wind energy potential for a given area, a variety of probability density functions (pdf) have been available in literature. In this paper, the bimodal mixture Weibull function (WW) and the probability function derived with maximum entropy principle (MEP) will be used and com- pared with the conventional Weibull function. Wind speed data measured at three wind farms experiencing different climatic environments in Taiwan are selected as sample data to test their performance. Judgment criterions include four kinds of statistical errors, i.e. the max error in Kolmogorov-Smirnov test, Chi-square error, root mean square error and relative error of wind potential energy. The results show that the proposed WW and MEP pdfs describe wind charac- terizations better than the conventional Weibull pdf, irrespective of wind speed and wind power density data, particularly for a location where wind regime presents two humps on it. For wind speed distributions, the WW pdf describes best according to the Kolmogorov-Smirnov test; while for wind power density, the MEP pdf outperforms the others.