Study on Power Transformers Fault Diagnosis Based on Wavelet Neural Network and D-S Evidence Theory

作     者：LIANG Liu-ming CHEN Wei-gen YUE Yan-feng WEI Chao YANG Jian-feng LIANG Liu-ming;CHEN Wei-gen;YUE Yan-feng

作者机构：State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing 400044 China Luoyang Power Supply Company Luoyang 471000 China 

出 版 物：《高电压技术》 (High Voltage Engineering)

年 卷 期：2008年第34卷第12期

页      面：2694-2700页

核心收录：

学科分类：12[管理学] 080801[工学-电机与电器] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Project Supported by National Natural Science Foundation of China ( 50777069 ) 

主　　题：小波神经网络 D-S证据理论 电力变压器 故障诊断 适应基因算法 

摘      要：Transformer faults are quite complicated phenomena and can occur due to a variety of *** have been several methods for transformer fault synthetic diagnosis,but each of them has its own limitations in real fault diagnosis *** order to overcome those shortcomings in the existing methods,a new transformer fault diagnosis method based on a wavelet neural network optimized by adaptive genetic algorithm(AGA)and an improved D-S evidence theory fusion technique is proposed in this *** proposed method combines the oil chromatogram data and the off-line electrical test data of transformers to carry out fault *** on the fusion mechanism of D-S evidence theory,the comprehensive reliability of evidence is constructed by considering the evidence importance,the outputs of the neural network and the expert *** new method increases the objectivity of the basic probability assignment(BPA)and reduces the basic probability assigned for uncertain and unimportant *** case study results of using the proposed method show that it has a good performance of fault diagnosis for transformers.