基于PCA降维和优化核参数SVM的电能质量扰动分类  

Classification of Power Quality Disturbances Based on PCA for Reducing the Dimensions and SVM with Optimal Kernel-parameter

作　　者：刘刚[1] 李凡光[1] 

机构地区：[1]上海电力学院自动化工程学院,上海200090

出　　处：《上海电力学院学报》2013年第2期163-168,共6页Journal of Shanghai University of Electric Power

基　　金：上海市教育委员会重点学科建设项目(J51301)

摘　　要：首先对采样信号用db4小波进行10层的多分辨分解,提取扰动信号各层能量与标准信号的能量差作为特征向量;然后用PCA对特征向量进行降维,取3维数据作为分类的特征向量,并将训练集采用交叉验证的方法自适应选择最优参数,并构造训练集模型;最后将测试集数据代入训练集模型进行分类测试.测试结果表明,在PCA降维后可以实现扰动的分类:分辨率高;抗噪能力强;适用于电能质量扰动的分类.Firstly,db4-wavelet is used to analyze PQD signals with multi-resolution decomposition of 10 layers,and energy differences of every level between PQD disturbance signal and standard signal as feature vectors;Then,it used PCA to analyze feature vectors and takes the front six dimensions are extracted as the eigenvectors of classification. The method of cross-validation is used for the training set to select the optimal parameters adaptively and constructs the training set model; finally ,the testing set is substituted into the training set model for classification. The test results show that this method can realize the classification of PQD, and has high resolution, strong resistance to noise, fast classification speed,and thus is suitable for the classification of PQD.

关 键 词：电能质量 小波能量差 主成分分析 支持向量机 自适应 

分 类 号：TM714[电气工程—电力系统及自动化] TM764.1