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The use of wind probability distributions derived from the maximum entropy principle in the analysis of wind energy. A case study

机译：从最大熵原理导出的风概率分布在风能分析中的使用。案例研究

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摘要

This paper analyses the use of a general probability distribution obtained through application of the maximum entropy principle (MEP), constrained by the low-order statistical moments of a given set of wind speed data, in the estimation of wind energy. For this purpose, a comparison is made between the two parameter Weibull distribution and the distributions obtained through the MEP. This comparison is based on an analysis of the level of fit to the cumulative frequencies of the hourly mean wind speeds recorded at weather stations located in the Canarian Archipelago. A comparison is also made of the ability to describe the experimental mean wind power density. The application of the probability plot correlation coefficient R~2, adjusted for degrees of freedom, shows that the Weibull distribution, whose parameters are estimated using the maximum likelihood principle, provide worse fits in all the cases analysed than those obtained through the maximum entropy distributions constrained by the low-order statistical moments. It is, thus, shown that maximum entropy distributions constrained by the three low-order statistical moments, in addition to representing the probabilities of observed periods of null wind speeds, offer less relative errors in determining the mean wind power density than the Weibull distribution. However, among other advantages of the Weibull distribution, is the greater simplicity of the calculations involved.
机译：本文分析了通过应用最大熵原理（MEP）获得的一般概率分布，该分布受给定风速数据集的低阶统计矩约束，用于估计风能。为此，在两个参数的威布尔分布和通过MEP获得的分布之间进行比较。该比较基于对加那利群岛上气象站记录的每小时平均风速累积频率的拟合度的分析。还对描述实验平均风能密度的能力进行了比较。对自由度进行调整的概率图相关系数R〜2的应用表明，使用最大似然原理估算其参数的威布尔分布在所有情况下均比通过最大熵分布获得的拟合更差。受低阶统计矩约束。因此，这表明受三个低阶统计矩约束的最大熵分布，除了表示观测到的零风速周期的概率外，在确定平均风能密度方面比韦布尔分布提供的相对误差较小。但是，除了威布尔分布的其他优点外，所涉及的计算也更加简单。

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