高电压技术2017，Vol.43Issue(11)：3668-3674,7.DOI:10.13336/j.1003-6520.hve.20171031023
基于粗糙集与多类支持向量机的电力变压器故障诊断
Fault Diagnosis for Power Transformer Based on Rough Set and Multi-class Support Vector Machine
摘要
Abstract
Some of the characteristic parameters are not effectively used in conventional fault diagnosis of transformer,which will cause the inaccuracy of diagnostic results.To improve the present situation,the core grounding current information is proposed to be combined with characteristic gases dissolved in oil as the input variables of one-against-one multi-class support vector machine in transformer fault diagnosis.Firstly,one-against-one multi-classes support vector machine is used to divide the fault category region.Secondly,the fault category region is described according to upper and lower approximation of rough set theory.Thirdly,the upper and lower approximation domain and the boundary domain of the fault classification are obtained,and the fault diagnosis classification rules are extracted.Finally,the attracted classification rules are used to realize fault classification.The proposed method realized integrating fault information,comprehensively utilizing the advantage of the performance of rough set theory in processing incomplete data,complex pattern depiction,and the advantage of good generalization performance of one-to-one support vector machine in the classification,so as to effectively improve the accuracy of fault classification.An example of transformer failure analysis show that the proposed method has higher diagnostic accuracy,and it can effectively reflect the incomplete information in the fault diagnosis.关键词
变压器/粗糙集/多类支持向量机/溶解气体分析/故障诊断/一对一Key words
transformer/rough set/multi-class support vector machine/dissolved gas analysis/fault diagnosis/one-versus-one引用本文复制引用
吴广宁,袁海满,宋臻杰,杨飞豹,高波,李帅兵..基于粗糙集与多类支持向量机的电力变压器故障诊断[J].高电压技术,2017,43(11):3668-3674,7.基金项目
国家自然科学基金(51177136).Project supported by National Natural Science Foundation of China (51177136). （51177136）
