Research On Transformer Fault Diagnosis Based On Artificial Neural Network

With the development of domestic power industry, new requirements of large transformer protection are presented. In the course of settling new types of transformer microcomputer protection, it is still a key and difficult problem how to prevent protection miss-operating caused by magnetizing inrush when transformer suddenly is closed in no load. So, this thesis analyses and compares some discriminating methods applied in practice or described in related literatures at present. On the basis of considering comprehensive factors, several methods to discriminate between internal faults and magnetizing inrush based on distributed artificial neural network, fuzzy neural network and artificial neural network with wavelet analysis are proposed. The simulative test results show that the training speed of NN model designed by the thesis is faster and the identification of magnetizing inrush based on these methods is effective.