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Polymers 2018, 10(10), 1096; https://doi.org/10.3390/polym10101096

A Novel Fault Diagnosis System on Polymer Insulation of Power Transformers Based on 3-stage GA–SA–SVM OFC Selection and ABC–SVM Classifier

1,2,†
,
1,2,†
,
1,2,* , 1,2,* and 3
1
Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China
2
National Demonstration Center for Experimental Electrical Engineering Education, Guangxi University, Nanning 530004, China
3
China Electric Power Research Institute, Haidian District, Beijing 100192, China
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Received: 24 September 2018 / Revised: 1 October 2018 / Accepted: 1 October 2018 / Published: 3 October 2018
(This article belongs to the Special Issue Polymers for Energy Applications)
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Abstract

Dissolved gas analysis (DGA) has been widely used in various scenarios of power transformers’ online monitoring and diagnoses. However, the diagnostic accuracy of traditional DGA methods still leaves much room for improvement. In this context, numerous new DGA diagnostic models that combine artificial intelligence with traditional methods have emerged. In this paper, a new DGA artificial intelligent diagnostic system is proposed. There are two modules that make up the diagnosis system. The two modules are the optimal feature combination (OFC) selection module based on 3-stage GA–SA–SVM and the ABC–SVM fault diagnosis module. The diagnosis system has been completely realized and embodied in its outstanding performances in diagnostic accuracy, reliability, and efficiency. Comparing the result with other artificial intelligence diagnostic methods, the new diagnostic system proposed in this paper performed superiorly. View Full-Text
Keywords: artificial bee colony (ABC); dissolved gas analysis (DGA); fault diagnosis; genetic algorithm (GA); power transformers; simulated annealing (SA) algorithm; support vector machine (SVM) artificial bee colony (ABC); dissolved gas analysis (DGA); fault diagnosis; genetic algorithm (GA); power transformers; simulated annealing (SA) algorithm; support vector machine (SVM)
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Huang, X.; Zhang, Y.; Liu, J.; Zheng, H.; Wang, K. A Novel Fault Diagnosis System on Polymer Insulation of Power Transformers Based on 3-stage GA–SA–SVM OFC Selection and ABC–SVM Classifier. Polymers 2018, 10, 1096.

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