Accurate wind resource assessment lies on the precise information provided by a probability distribution function (PDF). Therefore, it is an essential prerequisite to find the most appropriate PDF to model the wind speed data at the planning stage. Earlier, researchers have compared several distributions of 1, 2-parameters such as Rayleigh, Gamma, Exponential, Normal family, Weibull distributions, etc. Among these, 2-paramters Weibull distribution was a widely acceptable distribution for wind speed data modeling. However, its comparison with a multi-parameter (3 and 4 parameters) distribution has rarely been studied. In this paper, the Weibull distribution has been compared with four new distributions, which have rarely been studied for wind speed data modeling previously. They are 2-parameter Nakagami and Rician distribution, 4-parameter Johnson SB distribution, and 5-parameter Generalized Hyperbolic distribution. The sites selected for the case study are Trivandrum, Ahmedabad, Calcutta, Jaipur, New Delhi, and Port Blair of India. The result indicates that the Generalized Hyperbolic and Johnson distributions are ranked 1st and 2nd; Weibull and Nakagami distributions perform equally well and are ranked 3rd and 4th among the five compared distributions for five Indian stations. However, for one station (Ahmedabad), which is less skewed and has low kurtosis, the performance of Weibull distribution is better than those of the other distributions. The achieved results reveal that the skewness and kurtosis are equally important as the mean and standard deviation of wind speed data, which may influence the accuracy of the distribution. Wind behavior is stochastic, and a single distribution cannot be accepted as a universally accepted distribution for all locations of India.

中文翻译：

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用于风资源评估的不同多参数概率密度模型的比较

准确的风资源评估取决于概率分布函数 (PDF) 提供的精确信息。因此，在规划阶段找到最合适的PDF对风速数据进行建模是必不可少的先决条件。早前，研究人员比较过瑞利、伽玛、指数、正态族、威布尔分布等1、2参数的几种分布。其中，2参数威布尔分布是风速数据建模中被广泛接受的分布。然而，很少有人研究它与多参数（3 和 4 参数）分布的比较。在本文中，Weibull 分布与四种新的分布进行了比较，这些分布以前很少被研究用于风速数据建模。它们是 2 参数 Nakagami 和 Rician 分布，4 参数 Johnson SB 分布和 5 参数广义双曲线分布。为案例研究选择的地点是印度的特里凡得琅、艾哈迈达巴德、加尔各答、斋浦尔、新德里和布莱尔港。结果表明广义双曲线和约翰逊分布分别排在第 1 和第 2 位；Weibull 和 Nakagami 分布表现同样出色，在五个印度站的五个比较分布中分别排名第 3 和第 4 位。但是，对于偏度较小且峰度较低的站点（艾哈迈达巴德），威布尔分布的性能优于其他分布。所得结果表明，偏度和峰度与风速数据的均值和标准差同等重要，这可能会影响分布的准确性。风的行为是随机的，