风电场风速概率分布参数计算新方法

New Computation Method of Wind Speed Probability Distribution Parameters in Wind Farm

风能资源评估关系到风电的经济性和开发价值,一个地区的风速概率分布是该地区风能资源状况的最重要指标之一。在认为风电场风速服从双参数韦布尔(Weibull)分布前提下,为了提高参数计算精度,从智能化的角度提出尝试采用改进的微粒群算法对Weibull双参数进行建模和优化。由此参数估算能直接反映出风能资源特性的风能特征指标,与由常规最小二乘法、丹麦WAsP软件以及历史风速数据序列所计算的结果相比,实验表明该方法拟合精度更高,更接近实际风速状况,为风电场规划设计提供了更具价值的参考。

Wind speed probability distribution in a region is one of the most important indicators of wind energy resources condition in an area,because wind resource assessment is related to wind power＇s economic and development value.In order to improve computation precision of parmeters,a new computation method was proposed based on intelligence point in this paper.This method is tried to use modified particle swarm optimization algorithm to optimize the two parmeters of Weibull distribution.Wind indicators reflecting the wind energy resource characteristics are calculated according to these two optimal parameters.Compared to the results of conventional least squares method,Denmark WAsP software and historical wind speed data sequences,the proposed method has higher fitting precision and closer to actual wind conditions.It provides a more valuable reference to plan and design of wind farm.