高压电器2025，Vol.61Issue(2)：54-62,9.DOI:10.13296/j.1001-1609.hva.2025.02.007
基于KPCA-SVM的变压器多源信息融合故障诊断研究
Research on Multi-source Information Fusion Fault Diagnosis of Transformer Based on KPCA-SVM
摘要
Abstract
Transformer is one of the most important power transmission and transformation equipment in power sys-tem and its insulation status monitoring and fault identification are of great significance to the safe and stable opera-tion of power system.The partial discharge signal generated by the internal insulation deterioration of the transformer is currently one of the most effective criteria for evaluating the internal insulation status and identifying the fault type.In this paper,the ultrahigh frequency signal and ultrasonic signal under partial discharge are obtained by construct-ing four typical insulation defect models of transformer and setting up test platform for measurement.Throughout the analysis of two kinds of signals,two sets of characteristic parameters are extracted for ultrahigh frequency signals in both TRTD and PRPD modes,and a set of characteristic parameters are extracted for ultrasonic signals in TRTD mode.It is found after the feature fusion method of kernel principal component analysis together with support vector machine(KPCA-SVM)for information fusion that the recognition rate of information fusion is significantly improved compared to that of single information.关键词
变压器/特高频/超声波/信息融合/故障识别Key words
transformer/ultrahigh frequency/ultrasonic/information fusion/fault identification引用本文复制引用
杨旭,周文,程林,罗传仙,张静,江翼..基于KPCA-SVM的变压器多源信息融合故障诊断研究[J].高压电器,2025,61(2):54-62,9.基金项目
国家电网有限公司总部管理科技项目(超、特高压变压器油纸绝缘快速发展型故障检测与诊断关键技术研究).Project Supported by the State Grid Corporation Limited Headquarters Management Technology(Research on the Key Technology of Fault Detection and Diagnosis of Oil Paper Insulation of Super and UHV Transformers). （超、特高压变压器油纸绝缘快速发展型故障检测与诊断关键技术研究）
