改进VMD-PE的输电线路故障特征提取

Improved VMD-PE Fault Feature Extraction for Transmission Lines

针对三相交流电输电线路的故障信号分解存在误差,影响故障分类准确率的问题,为了提高故障信号分解的精细程度以及分类准确率,现基于故障电压信号提出一种改进的变分模态分解(VMD)-排列熵(PE)的故障特征提取的分类方法;通过MATLAB/Simulink搭建故障仿真模拟线路,生成故障数据集,为了得到最理想以及分解效果最好的组合,通过鲸鱼算法(WOA)优化对故障电压信号VMD的惩罚参数以及分解的个数进行求最优解组合,增加了各个分量分解的精度,采用同一变量法进行对比实验分析,分别利用VMD以及EMD对故障电压进行分解得到本征模态分量(IMF),结合排列熵(PE)对各个IMF进行计算,得到相应的特征向量,作为分类的依据,带入到高斯优化支持向量机(SVM)的决策树(DT)进行故障分类验证;通过仿真实验证明改进的VMD-PE对故障电压分解更加的具有可分辨性,同时相较于EMD-PE,识别率有很大的提升,极大程度的避免了混沌情况的发生,故障识别的准确率可高达96.7%,可以作为分解以及分类的依据。

There are errors in the fault signal decomposition of three-phase AC power transmission lines,which affect the accuracy of fault classification.In order to improve the precision of fault signal decomposition and classification accuracy,an improved variational mode decomposition(VMD)-permutation entropy(PE)fault feature extraction classification method based on fault voltage signal was proposed.MATLAB/Simulink was used to build fault simulation lines and generate fault data sets.In order to obtain the ideal and most effective combination for decomposition,whale optimization algorithm(WOA)was used to optimize the penalty parameters of fault voltage signal VMD and the number of decompositions to find the optimal solution combination,increasing the decomposition accuracy of each component.The same variable method was used for comparative experimental analysis.The VMD and EMD were used to decompose the fault voltage to obtain the intrinsic mode function(IMF),and the permutation entropy(PE)was used to calculate each IMF to obtain the corresponding feature vector,which was used as the basis for classification.Then,the decision tree(DT)of Gaussian optimized support vector machine(SVM)was used for fault classification verification.The simulation results show that the improved VMD-PE has more separability on the fault voltage decomposition,and the recognition rate is greatly improved compared with EMD-PE,which avoids the occurrence of chaos to a large extent.The accuracy of fault recognition is up to 96.7%,which can be used as the basis for the decomposition and classification.