高电压技术2017，Vol.43Issue(8)：2533-2540,8.DOI:10.13336/j.1003-6520.hve.20170731013
基于特征评估与核主元分析的电力变压器故障诊断
Fault Diagnosis of Power Transformer Based on Feature Evaluation and Kernel Principal Component Analysis
摘要
Abstract
The shortage of fault characteristic parameters as well as the limitation of fault information in power transformer fault diagnosis may result in an unsatisfied diagnosis result.To cope with such a problem,34 characteristics obtained by the combination with electrical test data and dissolved gas analyses(DGA) were taken as fault parameters to refine the fault characteristics.On such basis,the feature evaluation and kernel principal component analysis (KPCA) based fault diagnosis method was developed by a combination of the two approaches.By sensitive evaluation firstly,insensitive parameters were eliminated and thus help to weaken their influences on characteristics.Then,27 types of characteristics were conducted with the KPCA to reduce their dimensions.Finally,the extracted fault parameters with 9 dimensions were taken as the input vector of multiclass relevance vector machine (M-RVM) for fault classification.Case analysis shows that this method not only can compensate the deficiencies like shortage in fault feature parameters effectively,but also is more general,and fault identification accuracy is increased to 90.35％,which can provide a reference for the fault diagnosis of transformer fault case information limited.关键词
电力变压器/故障诊断/特征提取/特征评估/核主元分析/多分类相关向量机Key words
power transformer/fault diagnosis/feature extraction/feature evaluation/KPCA/M-RVM引用本文复制引用
吴广宁,袁海满,高波,李帅兵..基于特征评估与核主元分析的电力变压器故障诊断[J].高电压技术,2017,43(8):2533-2540,8.基金项目
国家自然科学基金(U1234202) （U1234202）
国家杰出青年基金(51325704).Project supported by National Natural Science Foundation of China (U1234202),National Fund for Distinguished Young Scientists of China (51325704). （51325704）
