计算技术与自动化2024，Vol.43Issue(2)：30-34,5.DOI:10.16339/j.cnki.jsjsyzdh.202402005
基于SVDD和改进K-Means的变压器故障诊断模型
Transformer Fault Diagnosis Model Based on SVDD and Improved K-Means
摘要
Abstract
The operation status of transformer is of great significance to the stability and reliability of intelligent distri-bution room.In order to realize the accurate diagnosis of transformer faults,based on the analysis of dissolved gases in transformer oil,a multi-classifier joint fault diagnosis method based on the combined use of support vector data description(SVDD)and improved K-Means clustering is proposed.First,SVDD is used to construct a closed classification surface to realize"normal"and"fault"judgments.Then K-Means clustering analysis is carried out on the"fault"samples,which are automatically divided into five types:low energy discharge,medium and low temperature overheat,high energy discharge,high temperature overheat and partial discharge.At the same time,the concept of local density is proposed to automatically determine the initial clustering center of K-Means to improve the clustering performance.Finally,the transformer fault data of the intelligent distribution room is used to carry out the verification experiment.The results show that compared with the traditional support vector machine(SVM)and BP neural network model,the fault diagnosis accuracy of the proposed meth-od is improved by 9.8%and 8%,respectively.关键词
智能配电房/变压器故障诊断/油中溶解气体分析/支持向量数据描述/多分类器联合Key words
intelligent distribution room/transformer fault diagnosis/analysis of dissolved gas in oil/support vector da-ta description/multi-classifier association分类
信息技术与安全科学引用本文复制引用
谢旭钦,刘泉辉,赵湘文,张清松,林剑雄,张帆..基于SVDD和改进K-Means的变压器故障诊断模型[J].计算技术与自动化,2024,43(2):30-34,5.基金项目
中国南方电网有限责任公司科技项目(082900KK52190001) （082900KK52190001）
