| 摘要: |
| 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。 |
| 关键词: 交流输电线路 变分模态分解 排列熵 决策树 |
| DOI： |
| 分类号: |
| 基金项目: |
|
|
|
XIE Xianle， YANG An
|
|
School of Electrical and Information Engineering Anhui University of Science and Technology Anhui Huainan 232001
China
|
| Abstract: |
| 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. |
| Key words: AC transmission line variational mode decomposition permutation entropy the decision tree |
