基于实测数据的电力系统过电压分类识别

Classification and Identification of Power System Overvoltages Based on Measured Data

构造了一个有效的基于实测数据的过电压自动分类识别树。首先抽取过电压信号的时域特征量,将过电压类别集合分为2个子集。其次对信号进行离散小波变换,抽取小波变换域特征量。为使小波变换域特征量更具区别性,对2个子集内的过电压信号采用不同的采样频率和小波分解层数。最后在分类树的各节点构造一个支持向量机二值分类器,采用实测过电压数据进行交叉验证。总识别率达95%,验证了分类树的有效性。

An effective classification and identification tree for automatically classifying overvoltages based on measured data is built.Firstly,the time domain features are extracted from three-phase overvoltage signals.The set of overvoltage category is classified into two subsets.Secondly,overvoltage signals are decomposed using discrete wavelet transform while other features are extracted from the wavelet transform domain.To make these features more distinctive,overvoltage signals belonging to different subsets each are resampled at different frequencies and decomposed to different resolution levels.Finally,binary classifiers based on the support vector machine are each built at a point on the classification tree and cross-validated using measured overvoltage data.The total identification rate is 95%,indicating that the classification tree can effectively classify overvoltage signals.