基于相关性和类间差异度的放电声信号特征量选择与降维  

Feature Selection and Dimensionality Reduction of Discharge Acoustic Signal Based on Correlation and Between-class Difference

作　　者：马建桥 杨广泽 曹盘盘 包艳艳 冯婷娜 MA Jianqiao;YANG Guangze;CAO Panpan;BAO Yanyan;FENG Tingna(School of Automation and Electrical Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China;State Grid Gansu Electric Power Research Institute,Lanzhou 730070,China;College of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,China;Hami Power Supply Company,State Grid Xinjiang Electric Power Co.,Ltd.,Hami 839000,China)

机构地区：[1]兰州交通大学自动化与电气工程学院,兰州730070 [2]国网甘肃省电力公司电力科学研究院,兰州730070 [3]兰州理工大学电气工程与信息工程学院,兰州730050 [4]国网新疆电力有限公司哈密供电公司,哈密839000

出　　处：《高电压技术》2023年第3期1194-1204,共11页High Voltage Engineering

基　　金：兰州交通大学天佑创新团队(TY202010);国网甘肃省电力公司电力科学研究院科技项目(52272219000Q)。

摘　　要：为准确高效地诊断电气设备放电故障类型,实现多维特征的有效降维,提出了基于相关性和类间差异度的特征量选择与降维方法。首先,搭建模拟放电可听声信号采集平台,利用交叉小波变换分析信号的相关主成分,获得信号特征频带及其对应的离散小波重构时域分量,提取不同类型放电声信号的多维时域特征;然后,利用Pearson相关系数矩阵分析特征量之间的相关性,结合各特征类间差异度和类内离散度,优选出特征量进行识别效果检验;接着,以参数优化的支持向量机识别准确率为维度选择判据,依据准确率变化规律确定最终维度和特征;最后将该文方法与传统降维算法进行对比,并探究不同干扰模式对该文方法的影响。结果表明:所提方法相对传统降维算法保留了原始特征属性,最终所选特征的识别准确率超过96%,为特征降维提供了有效判据。In order to accurately and efficiently diagnose the discharge fault types of electrical equipment and realize the effective dimensionality reduction of multi-dimensional features,a feature selection and dimensionality reduction method based on correlation and between-class difference is proposed in this paper.Firstly,an audible signal acquisition platform for analog discharge is built.The cross wavelet transform is used to analyze the relevant principal components of the sig-nal,to obtain the signal characteristic frequency band and its corresponding discrete wavelet reconstruction time-domain components,and to extract the multi-dimensional features in time domain of different types of discharge acoustic signals.Then,the Pearson correlation coefficient matrix is used to analyze the correlation between features.Combined with the between-class difference and within-class difference,the feature is selected to test the recognition effect.The recognition accuracy of parameter optimized support vector machine(SVM)is taken as the dimension selection criterion,and the fi-nal dimension and feature are determined according to the change law of accuracy.Finally,the method in this paper is compared with the traditional dimensionality reduction algorithm,and the influence of different interference modes on the method is explored.The results show that,compared with the traditional dimensionality reduction algorithm,the proposed method in this paper retains the original feature attributes,and the recognition accuracy of the final selected features ex-ceeds 96%,which provides an effective criterion for feature dimensionality reduction.

关 键 词：局部放电 可听声信号 交叉小波变换 类间差异度 特征降维 放电类型识别 

分 类 号：TM855[电气工程—高电压与绝缘技术]