The Transformer On-line Fault Diagnosis Based on Spectral Clustering Ensemble

被引：0

作者：

Liu R.-S. ^{[1
]}

Peng M.-F. ^{[1
]}

Xiao X.-H. ^{[2
]}

机构：

[1] College of Electrical and Information Engineering, Hunan University, Changsha, 410082, Hunan

[2] College of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, 410114, Hunan

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2017年 / 45卷 / 10期

关键词：

Dissolved gas analysis in oil; Fault diagnosis; Power transformer; Signal processing; Spectral clustering ensemble;

D O I：

10.3969/j.issn.0372-2112.2017.10.025

中图分类号：

学科分类号：

摘要：

To improve the accuracy of the transformer fault diagnosis based on dissolved gas analysis in oil(DGA), a transformer on-line fault diagnosis based on spectral clustering ensemble (TOFD-SCE) was proposed in this paper. The weighted double sampling algorithm create the samples set of the basic spectral clustering, which learned the local knowledge of the problems. The accuracy was improved by integrating the results of ensemble members, which were picked up form the basic spectral clustering in terms of the accuracy and variety. The conventional models are only trained by the historical data, and can't learn on-line. TOFD-SCE is trained and modified by both historical and new online data, and the accuracy is improved. The TOFD-SCE was validated by diagnosing the fault of SSP300000/500 transformers. Comparing with IEC three ratio, BP-neural networks and support vector machine, TOFD-SCE is more outstanding. © 2017, Chinese Institute of Electronics. All right reserved.

引用

页码：2491 / 2497

页数：6

共 16 条

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