Probability distribution of wind power volatility based on the moving average method and improved nonparametric kernel density estimation
Peizhe Xin, Ying Liu, Nan Yang, Xuankun Song, Yu Huang
Abstract
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.