基于DBN-IFCM的变压器故障诊断方法

被引：51

作者：

刘仲民 ^{[1
]}

翟玉晓 ^{[1
]}

张鑫 ^{[2
]}

周静龙 ^{[3
]}

机构：

[1] 兰州理工大学电气工程与信息工程学院

[2] 国网甘肃省电力公司检修公司

[3] 国网甘肃省电力公司白银供电公司

来源：

高电压技术 | 2020年 / 46卷 / 12期

关键词：

变压器; 故障诊断; 深度信念网络; 模糊C均值聚类; DBN-IFCM; 故障边界;

D O I：

暂无

中图分类号：

TM41 [电力变压器];

学科分类号：

080810 [电力电子与电能变换];

摘要：

针对变压器故障边界划分的模糊性和传统模糊方法对变压故障诊断准确率低等问题,提出了一种基于深度信念网络(deep belief network, DBN)和改进的模糊C均值聚类(improved fuzzy C-means clustering, IFCM)的变压器故障诊断方法。该方法首先对故障数据进行归一化处理,然后利用深度信念网络对故障数据进行特征提取,最后利用改进的模糊C均值聚类对提取的特征进行聚类,以达到故障分类的目的。仿真实验表明:相较已有的变压器故障诊断方法,所提方法具有较高的诊断准确率,其准确率为93.3%,能够较为精准地识别变压器的各种故障。

引用

收藏

页码：4258 / 4265

页数：8

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