Hybrid modified evolutionary particle swarm optimisation-time varying acceleration coefficient-artificial neural network for power transformer fault diagnosis |
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| Affiliation: | 1. Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia;2. UM Power Energy Dedicated Advanced Centre (UMPEDAC), Level 4, Wisma R&D UM, University of Malaya, Jalan Pantai Baharu, 59990 Kuala Lumpur, Malaysia;1. Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning, Guangxi 530004, China;2. State Grid Henan Electric Power Research Institute, Zhengzhou, Henan 450052, China;3. Shijiazhuang Power Supply Branch of State Grid Electric Power Company, Shijiazhuang 050093, China;4. Department of Electrical and Computer Engineering & Computer Science, University of New Haven, West Haven, CT 06516, USA;5. State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing 400044, China;6. National Demonstration Center for Experimental Electrical Engineering Education, Guangxi University, Nanning, Guangxi 530004, China;1. Department of Electrical and Computer Engineering, University of Canterbury, Christchurch 8041, New Zealand;2. Unison Networks Limited, Hastings 4156, New Zealand;1. College of Electrical and Information Engineering, Hunan University, Changsha 410082, China;2. Department of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;3. Electrical and Computer Engineering Department, Illinois Institute of Technology, Chicago 60616, USA;4. Center for Electric Power and Energy, Department of Electrical Engineering, Technical University of Denmark, Lyngby 2800, Denmark |
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| Abstract: | In power transformer fault diagnosis, dissolved gas analysis (DGA) has been widely used to identify the type of the fault. The common methods of DGA are IEC 60599 method, Doenenberg’s ratio method and Roger’s ratio method. The accuracy of the DGA diagnosis will determine the cost, duration and workload of the maintenance since it can influence the error in the maintenance. Although DGA methods have been used widely, sometimes they still yield incorrect diagnosis results. Thus, many works on transformer fault diagnosis have been proposed previously, which include artificial intelligence methods, to improve the accuracy of transformer fault diagnosis. However, the accuracy of the previously reported works is believed to have rooms for improvement. Therefore, in this work, hybrid modified evolutionary particle swarm optimisation-time varying acceleration coefficient (MEPSO-TVAC)-artificial neural network (ANN) was proposed for transformer fault diagnosis based on dissolved gas data. This is due to these two methods have never been proposed for transformer fault diagnosis in the past. The performance of the ANN was optimised through the proposed MEPSO-TVAC. The superiority of the proposed method was demonstrated through comparison with the existing DGA methods, unoptimised ANN and previously reported methods in literatures. The comparison shows that the proposed hybrid MEPSO-TVAC-ANN obtained the highest accuracy among all methods, which can then be used for power transformer fault diagnosis. |
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| Keywords: | Modified particle swarm optimisation Artificial neural network Power transformer Artificial intelligence |
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