基于自组织神经网络的变压器故障诊断  

Fault diagnosis of power transformers based on the neural network of self-organizing map

作　　者：顾民[1] 葛良全[1] 

机构地区：[1]成都理工大学核技术与自动化工程学院,四川成都610059

出　　处：《继电器》2007年第23期28-30,34,共4页Relay

基　　金：国家自然科学基金项目(40374051)~~

摘　　要：变压器故障诊断实质上是属于一种模式识别,基于类内样本与类中心的距离的不同而对类中心的偏移产生不同影响的思想,改善了传统ART2网络存在模式飘移的不足。首先对变压器DGA故障样本的输入矢量进行扩展处理,然后用改进后的ART2网络对样本进行诊断。通过与传统的三比值法和BP神经网络的诊断结果对比,验证了该方法的有效性和可行性。Fault Diagnosis of Power Transformers is a kind of pattern recognition. The paper modifies the traditional ART-2 network based on this idea： different distances for samples to center of cluster produce different influence on excursion of center of cluster and the lack of pattern drifting is improved, First the input vector of sample is extended, then it is diagnosed by the modified ART2-network .The results are compared with those obtained by the IEC three-ratio and BPNN method. Comparison result verify that the algorithm proposed is effective and feasibile. This paper is supported by National Natural Science Foundation of China（No.40374051）.

关 键 词：电力变压器 故障诊断 油中溶解气体分析 ART2网络 欧氏距离 

分 类 号：TM407[电气工程—电器]