Uncertainty Analysis of Wind Power Based on Operating Data

The uncertainty of wind power prediction has a serious impact on the stable operation of power system. In this paper, the actual operation data of 200 wind farms in a certain area of China are collected, and the wind farm stations are divided into different scale station clusters according to the integration location of the grid. The distribution characteristics of wind power prediction error of station clusters are analyzed. Then, based on the prediction error and power correlation characteristics, a clustering segmentation prediction error analysis method is proposed. Finally, the nonparametric kernel density estimation is used to fit the prediction error of station cluster, and the fitting precision evaluation indexes are compared to verify the applicability and effectiveness of the proposed method for the prediction error distribution of station cluster.