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| Research on transformer fault diagnosis based on a beetle antennae search optimized support vector machine |
| DOI：DOI: 10.19783/j.cnki.pspc.191534 |
| Key Words:transformer fault diagnosis BAS-SVM winding deformation MPSO-SVM |
| Author Name | Affiliation | | FANG Tao | 1. Luoyang Power Supply Company, State Grid Henan Electric Power Company, Luoyang 471000, China
2. State Grid Henan
Electric Power Research Institute, Zhengzhou 450052, China
3. Wuhan Kemov Electric Co., Ltd., Wuhan 430023, China | | QIAN Ye | 1. Luoyang Power Supply Company, State Grid Henan Electric Power Company, Luoyang 471000, China
2. State Grid Henan
Electric Power Research Institute, Zhengzhou 450052, China
3. Wuhan Kemov Electric Co., Ltd., Wuhan 430023, China | | GUO Canjie | 1. Luoyang Power Supply Company, State Grid Henan Electric Power Company, Luoyang 471000, China
2. State Grid Henan
Electric Power Research Institute, Zhengzhou 450052, China
3. Wuhan Kemov Electric Co., Ltd., Wuhan 430023, China | | SONG Chuang | 1. Luoyang Power Supply Company, State Grid Henan Electric Power Company, Luoyang 471000, China
2. State Grid Henan
Electric Power Research Institute, Zhengzhou 450052, China
3. Wuhan Kemov Electric Co., Ltd., Wuhan 430023, China | | WANG Zhihua | 1. Luoyang Power Supply Company, State Grid Henan Electric Power Company, Luoyang 471000, China
2. State Grid Henan
Electric Power Research Institute, Zhengzhou 450052, China
3. Wuhan Kemov Electric Co., Ltd., Wuhan 430023, China | | LUO Jianping | 1. Luoyang Power Supply Company, State Grid Henan Electric Power Company, Luoyang 471000, China
2. State Grid Henan
Electric Power Research Institute, Zhengzhou 450052, China
3. Wuhan Kemov Electric Co., Ltd., Wuhan 430023, China | | BA Quanke | 1. Luoyang Power Supply Company, State Grid Henan Electric Power Company, Luoyang 471000, China
2. State Grid Henan
Electric Power Research Institute, Zhengzhou 450052, China
3. Wuhan Kemov Electric Co., Ltd., Wuhan 430023, China |
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| Abstract:In order to accurately judge whether a transformer winding has faults and ensure the reliability of power supply of the transformer, a method of transformer winding fault diagnosis based on BAS-SVM is proposed. It uses an SVM as the classifier of the transformer winding deformation degree, and optimizes the kernel function and penalty factor of the SVM by using a beetle antennae search algorithm. The BAS-SVM is trained by artificial experience to ensure that the algorithm has a high accuracy of fault diagnosis. In order to compare the advantages of the BAS-SVM algorithm in this application, a Modified Particle Swarm Optimization (MPSO) is also used to optimize SVM. The simulation results show that the fault diagnosis accuracy rate of the BAS-SVM algorithm is 10% higher than that of MPSO-SVM algorithm. Finally, the effectiveness of the BAS-SVM method on transformer winding fault diagnosis is verified by an example.
This work is supported by Science and Technology Project of the Headquarter of State Grid Corporation of China (No. 52170218000M) and Science and Technology Project of State Grid Henan Electric Power Company in 2019 (No. 5217A01801U5). |
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