Transformer Fault Diagnosis Based on Homotopy BP Algorithm

摘要：

Power transformer fault diagnosis is the key technology of electric power system. To solve the problem that BP neural network easily trapped in local minima points, a non-linear homotopy based BP neural network is introduced in power transformer fault diagnosis. The neural network parameters were chosen after several experiments. LM optimization algorithm trained the non-linear homotopy BP neural network DAG data was processed by cumulative frequency method and sent to BP neural network. The neural network proposed in this paper had a better performance on convergent speed and avoid trapped in local minima points. The power transformer fault diagnosis experiments and gases regression curve analysis both demonstrate that fault diagnosis precision of non-linear BP neural network was higher than standard BP network.

关键词： power transformer fault diagnosis dissolved gas analysis BP neural network homotopy algorithm

作者: Jiyin Zhao Ruirui Zheng Jianpo Li

作者单位: college of electromechanical information engineering,Dalian Nationalities University,Dalian,China college of communication engineering,Jilin University,Changchun,China college of information engineering,Northeast Dianli University,Jilin,China

会议类型: 国际会议

会议名称: 2009 9th International Conference on Electronic Measurement & Instruments(第九届电子测量与仪器国际会议 ICEMI2009)

会议地点: 北京

会议语种:英文

页码: 3850-3854

在线出版日期: 2009-08-16（万方平台首次上网日期，不代表论文的发表时间）