A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM
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 metho...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/21/4170 |
| _version_ | 1811300414476255232 |
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| author | Bing Zeng Jiang Guo Wenqiang Zhu Zhihuai Xiao Fang Yuan Sixu Huang |
| author_facet | Bing Zeng Jiang Guo Wenqiang Zhu Zhihuai Xiao Fang Yuan Sixu Huang |
| author_sort | Bing Zeng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T06:50:49Z |
| format | Article |
| id | doaj.art-f45f0b56aef045f59edd096b65c13b73 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T06:50:49Z |
| publishDate | 2019-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-f45f0b56aef045f59edd096b65c13b732022-12-22T02:57:25ZengMDPI AGEnergies1996-10732019-11-011221417010.3390/en12214170en12214170A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVMBing Zeng0Jiang Guo1Wenqiang Zhu2Zhihuai Xiao3Fang Yuan4Sixu Huang5Intelligent Power Equipment Technology Research Center, Wuhan University, Wuhan 430072, ChinaIntelligent Power Equipment Technology Research Center, Wuhan University, Wuhan 430072, ChinaIntelligent Power Equipment Technology Research Center, Wuhan University, Wuhan 430072, ChinaCollege of Power & Mechanical Engineering, Wuhan University, Wuhan 430072, ChinaIntelligent Power Equipment Technology Research Center, Wuhan University, Wuhan 430072, ChinaIntelligent Power Equipment Technology Research Center, Wuhan University, Wuhan 430072, ChinaDissolved 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.https://www.mdpi.com/1996-1073/12/21/4170grey wolf optimizerdifferential evolutiondissolved gas analysistransformer fault diagnosisleast square support vector machinekernel principal component analysis |
| spellingShingle | Bing Zeng Jiang Guo Wenqiang Zhu Zhihuai Xiao Fang Yuan Sixu Huang A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM Energies grey wolf optimizer differential evolution dissolved gas analysis transformer fault diagnosis least square support vector machine kernel principal component analysis |
| title | A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM |
| title_full | A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM |
| title_fullStr | A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM |
| title_full_unstemmed | A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM |
| title_short | A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM |
| title_sort | transformer fault diagnosis model based on hybrid grey wolf optimizer and ls svm |
| topic | grey wolf optimizer differential evolution dissolved gas analysis transformer fault diagnosis least square support vector machine kernel principal component analysis |
| url | https://www.mdpi.com/1996-1073/12/21/4170 |
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