基于小波变换和支持向量机的风电功率爬坡事件识别与预测

An Approach for Wind Power Climbing Event Identification and Prediction Based on Wavelet and SVM

随着风电场规模的增大,风电功率爬坡事件给电网带来的影响越来越显著,提高爬坡事件识别与预测精度对电网安全经济运行具有重要意义。阐述了爬坡事件的定义,提出了基于小波变换(WT)的风电功率爬坡事件识别方法,建立了风电功率爬坡事件的WT-SVM预测模型。以某风电场的实测风电功率数据为例,进行不同时段的识别与预测。结果表明,基于WT的方法可以快速准确地识别风电功率爬坡事件及其特征值,WT-SVM爬坡事件预测模型可以提高风电功率爬坡事件预测准确度。

With the increasing scale of the wind farm, wind power climbing events to the effects of the power grid is more and more significant. This paper expounds tile definition of climbing event, a recognition method based on wavelet transform（WT） was proposed for wind power climbing event, a WT-SVM prediction model was established for wind power climbing event. The examples use wind power data of a wind farm, and the identification and prediction of the wind power are carried out at different times. The results show that, the method based on WT can quickly and accurately identify wind power climbing event and its characteristic value,the WT-SVM prediction model can improve the wind power climbing event prediction accuracy.