基于ST-SVD-PCA的串联故障电弧特征提取方法

为深入研究煤矿井下串联故障电弧特征及提取方法,分别以电动机和变频器负载为研究对象,开展不同电流条件下的串联型故障电弧实验。采用S变换(ST)对回路电流进行时频域变换,求得S变换矩阵的幅值矩阵作为特征矩阵;对特征矩阵进行奇异分解(SVD),得到矩阵的奇异值;对多组奇异值组成的特征向量进行主元分析(PCA),选取累积贡献率高于95%的主元作为故障识别的特征,实现特征向量的降维;最后采用遗传算法(GA)优化的支持向量机(SVM)算法对故障电弧特征的有效性进行了测试。通过不同负载和工况条件实验,进一步验证了基于上述故障电弧特征的故障电弧识别方法的兼容性,该方法可以有效识别电机及变频器负载回路的串联故障电弧。

Feature extraction method of series fault arc based on ST-SVD-PCA

In order to study the characteristics and extract methods of series fault arc in underground coal mine power supply system,a series of fault arc experiments were carried out in motor and inverter load respectively.The time-frequency domain transform for loop current signal was conducted by using S-transform (ST).The amplitude matrix of S-transform was used as time-frequency feature matrix.The matrix singular value was obtained by conducting singular value decomposition (SVD) of the feature matrix.To reduce dimensions of feature vector,the principal component analysis (PCA) was carried out.The feature vector consists of many groups of singular value.The main component whose cumulative contribution rate higher than 95% was selected as fault feature.The validity of the extracted fault arc features were tested by using genetic algorithm (GA) optimized support vector machine (SVM).The compatibility of the arc fault identification method based on those fault arc features was also tested under different loads and operating conditions.It showed that the method can effectively identify the series arc fault occurred in motor and inverter load circuit.