基于波动过程聚类的风电功率预测极大误差估计方法

估计风电功率预测中可能发生的极大误差，有助于优化含风电电力系统的运行调度，提高电网对大规模风电的接纳能力。根据对历史风电功率预测误差分布特征的分析，提出了基于风电预测出力波动过程聚类的极大误差估计方法。首先利用摇摆窗对风电功率预测数据划分不同的波动过程，在此基础上，通过分析预测出力的波动性和功率水平与预测误差分布的相关性，聚类相似分布特性的预测误差，然后利用滑动窗宽的核密度方法拟合预测误差概率密度并估计极大误差。最后以美国BPA地区的风电功率数据为实例，对不同估计方法进行了较全面的分析，验证了该方法的有效性。

An estimation method for wind power prediction great error based on clustering fluctuation process

Estimating the great error in wind power prediction contributes to optimizing scheduling of power system which contains wind power and improving the ability of the power grid to accommodate large-scale wind power plant. According to the analysis of the error distribution of historical wind power prediction, an approach to estimating the great error based on clustering wind power fluctuation process is proposed. Firstly, wind power prediction data is divided into diverse fluctuation processes by swinging door algorithm, and on this basis, cluster prediction errors of the same distribution by analyzing the correlation between the fluctuation and the amplitude of wind power and the distribution of prediction errors. Then this paper fits probability density distribution of the prediction errors and estimates the great error adopting slide bandwidth kernel density estimation method. Finally, the wind power data of BPA in the United States is taken as example, the effectiveness of this method is validated by comprehensively analyzing different methods. This work is supported by National Natural Science Foundation of China (No. 51207115).