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

doi:10.3969/j.issn.0372-2112.2017.10.025

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ACTA ELECTRONICA SINICA ›› 2017, Vol. 45 ›› Issue (10) : 2491-2497. DOI: 10.3969/j.issn.0372-2112.2017.10.025

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

LIU Rong-sheng¹, PENG Min-fang¹, XIAO Xiang-hui²

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Abstract

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.

Key words

fault diagnosis / power transformer / spectral clustering ensemble / dissolved gas analysis in oil / signal processing

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LIU Rong-sheng, PENG Min-fang, XIAO Xiang-hui. The Transformer On-line Fault Diagnosis Based on Spectral Clustering Ensemble[J]. Acta Electronica Sinica, 2017, 45(10): 2491-2497. https://doi.org/10.3969/j.issn.0372-2112.2017.10.025

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Funding

National Natural Science Foundation of China (No.61472128, No.61173108); Key Program of Natural Science Foundation of Hunan Province, China (No.14JJ2150); Science and Technology Program of State Grid Corporation of China (No.5216A514001K)