南方电网技术2025，Vol.19Issue(1)：20-29,10.DOI:10.13648/j.cnki.issn1674-0629.2025.01.003
基于KPCA-IPOA-LSSVM的变压器电热故障诊断
Electrical and Thermal Fault Diagnosis of Transformer Based on KPCA-IPOA-LSSVM
摘要
Abstract
In order to solve the problem of low accuracy of fault diagnosis of oil-immersed transformers,a transformer fault diagnosis method of kernel principal component analysis(KPCA)with improved pelican optimization algorithm(IPOA)optimized least squares support vector machine(LSSVM)is proposed.Firstly,KPCA is used to extract features from multidimensional transformer fault data,reducing computational complexity.Secondly,logistic chaotic mapping,adaptive weight strategy,and lens imaging reverse learning strategy are introduced to improve the pelican optimization algorithm(POA).Finally,the KPCA-IPOA-LSSVM fault diagnostic model is established,and the diagnostic accuracy is 94.24%.Compared with the PCA-IPOA-SVM,KPCA-IPOA-SVM,KPCA-WOA-LSSVM,and KPCA-POA-LSSVM fault diagnostic models,the accuracy is improved respectively by 18.31%,11.53%,11.87%,7.46%.The results show that the transformer fault diagnosis model proposed in this paper effectively improves the accuracy of fault diagnosis,proving that the diagnostic model has certain significance in theoretical research and practical engineering application.关键词
变压器/鹈鹕优化算法/最小二乘支持向量机/核主成分分析/故障诊断Key words
transformer/pelican optimization algorithm/least squares support vector machine/kernel principal component analysis/fault diagnosis分类
动力与电气工程引用本文复制引用
陈尧,周连杰..基于KPCA-IPOA-LSSVM的变压器电热故障诊断[J].南方电网技术,2025,19(1):20-29,10.基金项目
国家自然科学基金资助项目(51974151).Supported by the National Natural Science Foundation of China(51974151). （51974151）
