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A Novel Fault Diagnosis Method for Power Transformer Based on Dissolved Gas Analysis Using Hypersphere Multiclass Support Vector Machine and Improved D–S Evidence Theory
Open AccessArticle

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

by 1,2, 1,2,*, 1,2, 2, 1,2 and 1,2
1
Intelligent Power Equipment Technology Research Center, Wuhan University, Wuhan 430072, China
2
College of Power & Mechanical Engineering, Wuhan University, Wuhan 430072, China
*
Author to whom correspondence should be addressed.
Energies 2019, 12(21), 4170; https://doi.org/10.3390/en12214170
Received: 28 September 2019 / Revised: 29 October 2019 / Accepted: 30 October 2019 / Published: 1 November 2019
(This article belongs to the Special Issue Power Transformer Condition Assessment)
Dissolved gas analysis (DGA) is a widely used method for transformer internal fault diagnosis. However, the traditional DGA technology, including Key Gas method, Dornenburg ratio method, Rogers ratio method, International Electrotechnical Commission (IEC) three-ratio method, and Duval triangle method, etc., suffers from shortcomings such as coding deficiencies, excessive coding boundaries and critical value criterion defects, which affect the reliability of fault analysis. Grey wolf optimizer (GWO) is a novel swarm intelligence optimization algorithm proposed in 2014 and it is easy for the original GWO to fall into the local optimum. This paper presents a new meta-heuristic method by hybridizing GWO with differential evolution (DE) to avoid the local optimum, improve the diversity of the population and meanwhile make an appropriate compromise between exploration and exploitation. A fault diagnosis model of hybrid grey wolf optimized least square support vector machine (HGWO-LSSVM) is proposed and applied to transformer fault diagnosis with the optimal hybrid DGA feature set selected as the input of the model. The kernel principal component analysis (KPCA) is used for feature extraction, which can decrease the training time of the model. The proposed method shows high accuracy of fault diagnosis by comparing with traditional DGA methods, least square support vector machine (LSSVM), GWO-LSSVM, particle swarm optimization (PSO)-LSSVM and genetic algorithm (GA)-LSSVM. It also shows good fitness and fast convergence rate. Accuracies calculated in this paper, however, are significantly affected by the misidentifications of faults that have been made in the DGA data collected from the literature. View Full-Text
Keywords: grey wolf optimizer; differential evolution; dissolved gas analysis; transformer fault diagnosis; least square support vector machine; kernel principal component analysis grey wolf optimizer; differential evolution; dissolved gas analysis; transformer fault diagnosis; least square support vector machine; kernel principal component analysis
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MDPI and ACS Style

Zeng, B.; Guo, J.; Zhu, W.; Xiao, Z.; Yuan, F.; Huang, S. A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM. Energies 2019, 12, 4170. https://doi.org/10.3390/en12214170

AMA Style

Zeng B, Guo J, Zhu W, Xiao Z, Yuan F, Huang S. A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM. Energies. 2019; 12(21):4170. https://doi.org/10.3390/en12214170

Chicago/Turabian Style

Zeng, Bing; Guo, Jiang; Zhu, Wenqiang; Xiao, Zhihuai; Yuan, Fang; Huang, Sixu. 2019. "A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM" Energies 12, no. 21: 4170. https://doi.org/10.3390/en12214170

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