绝缘子污秽放电的声发射核主成分诊断法  

Acoustic Emission Diagnosis Method of Insulator's Pollution Discharge Based on Kernel Principle Component Analysis

作　　者：李自品[1] 舒乃秋[1] 李红玲[1] 汪游胤[1] 

机构地区：[1]武汉大学电气工程学院,武汉430072

出　　处：《高电压技术》2012年第11期3008-3014,共7页High Voltage Engineering

基　　金：国家重点基础研究发展计划(973计划)(2009CB724500)~~

摘　　要：为了提高污秽绝缘子外绝缘状态的诊断准确度,利用绝缘子污秽放电时产生的声发射信号评定其外绝缘状态。通过绝缘子污秽试验,由高灵敏度声信号监测装置检测绝缘子的污秽放电声发射信号;对提取的声发射信号进行核主成分分析,将样本从低维的状态空间非线性的映射到高维核空间,在核空间采用随机森林方法训练得到分类器群,根据分类器群的分类结果对每个测试样本进行投票表决决定其最终分类。分析和诊断试验结果表明,声发射信号的3个原始特征量经核主成分分析后,变换为65个核特征量,有效地提高了分类器群之间的差异性。基于核主成分分析的随机森林模型的状态诊断结果具有很高的准确性。利用污秽放电声发射信号可进行污秽放电阶段的划分,以达到监测绝缘子的外绝缘状态的目的。External insulation status assessment of contaminated insulator is proposed in this paper using the acoustic emission signals generated when the polluted insulator flashover discharges to improve the accuracy of diagnosis on insulator＇s external insulation.Through artificial contamination experiments,the acoustic emission signals generated from a polluted insulator were monitored by sound monitoring devices with high sensitivity.The acoustic emission signals were analyzed by using KPCA（kernel principle component analysis）,so as to increase the number of features.Then random forests were constructed to get classifier groups in high dimensional kernel space.Finally,according to the voting result of the classifier groups,the final classification of the testing sample was gained.The analysis and experimental results show that,by converting the 3original features of the acoustic emission signals to 65kernel features through KPCA,the difference among classifier groups was improved effectively,and results of the state diagnosis based on KPCA and random forests had a higher accuracy.By using the acoustic emission signals generated from the polluted insulator discharge,the discharge phase can be distinguished,which realizes the monitoring of the external insulation status of insulators.

关 键 词：绝缘子 声发射信号 核主成分分析(KPCA) 随机森林 污秽放电 诊断 

分 类 号：TM855[电气工程—高电压与绝缘技术]