基于模糊ART神经网络的变压器局部放电模式识别  

Pattern Recognition of Transformer Partial Discharge Based on Fuzzy ART Neural Network

作　　者：王家炜[1] 杨燕翔[1] 孟海林[1] 陈伟[1] 

机构地区：[1]西华大学电气信息学院,四川成都610039

出　　处：《西华大学学报（自然科学版）》2013年第6期79-83,共5页Journal of Xihua University:Natural Science Edition

摘　　要：对变压器主要放电类型的3种局部放电典型电极模型进行试验,通过分析计算提取局部放电信号特征量,建立相应的局部放电信号特征库,以此作为模糊ART网络的输入,对局部放电的类型进行模式识别,并将识别结果与BP网络的识别结果进行对比。实验结果表明,模糊ART网络用于变压器局部放电模式识别是有效的。The authors conducted model test by using three typical electrode mode of partial discharge, Through analysis and cal- culation the partial discharge signal characteristics were extracted, and the corresponding partial discharge signals feature library was established. The extracted feature amount of partial discharge signals was used as the fuzzy ART networkg input in order to identify the type of partial discharge, and compare with the recognition results of the BP neural network. The results of experiment showed that fuzzy ART neural network was effective for pattern recognition of transformer partial discharge.

关 键 词：变压器 局部放电 模糊ART神经网络 模式识别 

分 类 号：TM41[电气工程—电器] TP181[自动化与计算机技术—控制理论与控制工程]