Transformer Internal Insulation Fault Diagnosis Based on RBF Neural Network Evolved by Immune Particle Swarm Optimization
The reliability of the power transformer operation is directly related to the security of power system and the reliability of power supply. In order to improve the diagnosis accuracy of internal insulation fault in transformer, this paper proposes an algorithm of transformer internal insulation fault diagnosis which is based on RBF neural network evolved by immune particle swarm optimization by analyzing the internal insulation fault type of transformer and the content of dissolved gas in transformer oil composition. The paper focuses on the composition principle of transformer fault diagnosis based on RBF neural network. The method of determining the number of hidden layer of network center and the initial position based on artificial immune network algorithm is given. The method of network weight optimization based on particle swarm optimization algorithm is developed. And the simulation experiment is also given. The simulation results show that the proposed algorithm can effectively diagnose the transformer fault types and the diagnosis accuracy can reach above 90