摘要
本文研究了一种基于BP神经网络的变压器故障诊断方法, 利用油中气体含量分析的方法, 收集整理变压器故障信息作为训练和诊断样本, 建立了基于BP神经网络的变压器故障诊断模型, 准确率最高达到95%, 具有较高的实际应用价值.
In this paper, we studied a kind of transformer fault diagnosis method based on BP neural net.work, using analysis method of gas content, collected transformer fault information as the training samples,and diagnosised based on BP neural network model of transformer fault diagnosis, 96% accurate, feasible in practical application.
作者
黄明增
高泷森
林广大
HUANG Mingzeng;Gao Longsen;LIN Guangda(College of Engineering, South China Agricultural University,Guangzhou Guangdong 510642;College of Information Engineering, Hebei Geography University,Shijiazhuang Hebei 050031)
出处
《河南科技》
2018年第7期49-51,共3页
Henan Science and Technology
关键词
油浸式变压器
BP神经网络
故障诊断
oil-immersed transformer；BP nerve network；fault diagnosis
