Power transformer fault diagnosis using FCM and improved PCA

被引：15

作者：

Kari, Tusongjiang ^{[1
]}

Gao, Wensheng ^{[1
]}

机构：

[1] Department of Electrical Engineering, Tsinghua University, Beijing,100084, China

来源：

Journal of Engineering | 2017年 / 2017卷 / 14期

关键词：

Electric fault currents - Fault detection - Power transformers - Electric transformer testing - Failure analysis - Clustering algorithms;

D O I：

10.1049/joe.2017.0851

中图分类号：

学科分类号：

摘要：

In order to improve fault diagnosis accuracy of power transformer, a new fault diagnosis model based on fuzzy C-means (FCM) clustering algorithm and improved principal component analysis (IPCA) is presented. First, dissolve gas analysis samples are clustered with FCM and cluster centre for each fault type is regarded as reference sequence. Then, the IPCA approach is implemented to obtain main principal components containing 95% of original information. Finally, Euclidean distances between principal components of reference sequence and testing sample are calculated to identify final fault type. Cases studies and test results show that the proposed approach achieves recognition of transformer fault effectively and has a higher diagnostic accuracy than the international electrotechnical commission (IEC) ratio method and the improved three ratio method. © 2017 Institution of Engineering and Technology. All Rights Reserved.

引用

页码：2605 / 2608

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