高压电器2024，Vol.60Issue(5)：99-107,9.DOI:10.13296/j.1001-1609.hva.2024.05.013
基于GJO特征量优选的AO-RF的变压器故障诊断模型
Fault Diagnosis Model of Transformer Based on GJO Feature Optimization and AO-RF
摘要
Abstract
In the fault diagnosis process of transformer,reasonable feature selection is contribute to improve the diag-nostic accuracy of the diagnosis model.For that,a fault diagnosis model of transformer based on GJO feature optimiza-tion and AO-RF is proposed in this paper.Firstly,the golden Jackal optimization(GJO)algorithm is used to optimize the characteristic quantity of 21-d transformer dissolved gas-in-oil;Then,according to the feature optimization re-sults obtained by GJO,the fault diagnosis model of transformer with random forest(RF)optimized by the aquila opti-mization(AO)algorithm is used to diagnose the fault of transformer.And it is compared with the diagnosis results that have different feature parameters and different fault diagnosis models.The experimental result shows that the fault diagnosis accuracy of the GJO optimized feature,compared with 21-d original feature,three-ratio method,non-coded ratio method and AO optimized feature,can be improved by 1.12%-25.78%,and the Kappa coefficient can be increased by 0.02-0.24;The fault diagnosis accuracy of AO-RF,compared with RF,SVM,ELM,SSA-RF,WOA-RF and GJO-RF models,can be increased by 1.84%-15.86%,and the Kappa coefficient can be increased by 0.02-0.16,which verify the effectiveness and accuracy of the proposed method.关键词
变压器/故障诊断/金豺算法/随机森林/天鹰算法Key words
transformer/fault diagnosis/golden Jackal optimization(GJO)/random forest(RF)/aquila optimizer(AO)引用本文复制引用
叶育林,刘森,黄松,韩晓慧,杜振斌,李彬,吕杰,薛杨,赵春琳..基于GJO特征量优选的AO-RF的变压器故障诊断模型[J].高压电器,2024,60(5):99-107,9.基金项目
河北省省级科技计划资助(20312101D). Project Supported by S&T Program of Hebei(20312101D). （20312101D）
