Probability distribution of wind power volatility based on the moving average method and improved nonparametric kernel density estimation

In the process of large-scale,grid-connected wind power operations,it is important to establish an accurate probability distribution model for wind farm fluctuations.In this study,a wind power fluctuation modeling method is proposed based on the method of moving average and adaptive nonparametric kernel density estimation(NPKDE)method.Firstly,the method of moving average is used to reduce the fluctuation of the sampling wind power component,and the probability characteristics of the modeling are then determined based on the NPKDE.Secondly,the model is improved adaptively,and is then solved by using constraint-order optimization.The simulation results show that this method has a better accuracy and applicability compared with the modeling method based on traditional parameter estimation,and solves the local adaptation problem of traditional NPKDE.

风轮特性的电气模拟

通过对风轮的受力及风能利用系数进行分析,推导了风能利用系数和风轮转矩的近似表达式,讨论了实现风轮输出特性及转动惯量模拟的方法。使用感应电机对风轮的转矩转速特性进行模拟,同时给出了风轮模拟系统的硬件结构,搭建了实验平台。测试表明,该系统能够实现风轮输出特性的近似模拟,并能较准确地反映出转动惯量对输出特性的影响,为风力发电的研究提供了方便的测试手段。