基于Weibull和对数逻辑分布的两个新型风速T-X模型的比较分析：用于伊朗大不里士风能潜力估计
Comparative analysis of two new wind speed T-X models using Weibull and log-logistic distributions for wind energy potential estimation in Tabriz, Iran
AI总结：
本文提出基于T-X族的WE3-LL3和LL3-WE3两种新型风速分布模型，通过最大似然估计与多种检验，在伊朗大不里士数据上验证其优于传统PDF，用于风能潜力估计。
摘要：
为评估特定区域的风能潜力，通常使用统计分布函数来表征风速分布。选择合适的风速模型对于最小化风能估算误差至关重要。本文提出了一种新方法，利用T-X连续分布族生成两个此前在风能文献中未被探索的新型风速分布函数。这两个统计分布，即Weibull三参数-对数逻辑三参数（WE3-LL3）和对数逻辑三参数-Weibull三参数（LL3-WE3），与另外四个概率密度函数（PDFs）进行比较，以分析在伊朗大不里士收集的风速数据。所考虑分布的参数通过使用Nelder-Mead数值方法的最大似然估计器进行估计。基于均方根误差、决定系数、Kolmogorov-Smirnov检验和卡方检验等标准，评估了所提出分布对实际风速数据的适用性。分析结果表明，LL3-WE3分布在捕捉季节性和年度风速数据方面总体上表现出更优的性能（夏季除外），而WE3-LL3分布对夏季数据拟合最佳。同时观察到，就风功率密度误差标准而言，LL3-WE3和WE3-LL3分布均能有效描述风速数据。总体而言，与其他PDF相比，LL3-WE3和WE3-LL3模型在估算风能潜力方面提供了高度准确的拟合。
英文摘要：
To assess the potential of wind energy in a specific area, statistical distribution functions are commonly used to characterize wind speed distributions. The selection of an appropriate wind speed model is crucial in minimizing wind power estimation errors. In this paper, we propose a novel method that utilizes the T-X family of continuous distributions to generate two new wind speed distribution functions, which have not been previously explored in the wind energy literature. These two statistical distributions, namely the Weibull-three parameters-log-logistic (WE3-LL3) and log-logistic-three parameters-Weibull (LL3-WE3) are compared with four other probability density functions (PDFs) to analyze wind speed data collected in Tabriz, Iran. The parameters of the considered distributions are estimated using maximum likelihood estimators with the Nelder-Mead numerical method. The suitability of the proposed distributions for the actual wind speed data is evaluated based on criteria such as root mean square errors, coefficient of determination, Kolmogorov-Smirnov test, and chi-square test. The analysis results indicate that the LL3-WE3 distribution demonstrates generally superior performance in capturing seasonal and annual wind speed data, except for summer, while the WE3-LL3 distribution exhibits the best fit for summer. It is also observed that both the LL3-WE3 and WE3-LL3 distributions effectively describe wind speed data in terms of the wind power density error criterion. Overall, the LL3-WE3 and WE3-LL3 models offer a highly accurate fit compared to other PDFs for estimating wind energy potential.
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