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LI Hao,WANG Fuzhong,WANG Rui.Identification Algorithm for Transformer Insulation Fault Types Based on Improved RBF Neural Network[J].JOURNAL OF POWER SUPPLY,2018,16(5):167-173
Identification Algorithm for Transformer Insulation Fault Types Based on Improved RBF Neural Network
Received:August 07, 2016  Revised:January 11, 2018
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DOI:10.13234/j.issn.2095-2805.2018.5.167
Keywords:power transformer  fault diagnosis  RBF neural network  artificial immune network  particle swarm optimization algorithm
Fund Project:Research Foundation of Henan Province
        
AuthorInstitutionEmail
LI Hao School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo , China 1548905212@qq.com
WANG Fuzhong School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo , China
WANG Rui School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo , China
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Abstract:
      To accurately diagnose the internal latent fault types of a power transformer, a novel radial basis function(RBF) neural network algorithm is proposed by analyzing the gas production under eight latent internal insulation fault types, such as oil overheating and partial discharging in oil paper insulation. This algorithm is improved by artificial immune network algorithm and particle swarm optimization algorithm. This paper focuses on the composition principle of transformer fault diagnosis model based on RBF neural network, the method for determining the center of hidden layer in the fault model based on artificial immune network algorithm, and the method of network weight optimization based on particle swarm optimization algorithm. Simulation experiments are carried out, showing that the proposed algorithm can effectively identify the insulation fault types at an accuracy of higher than 90%.
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