The transformer fault diagnosis based on Quantum Neural Network

It is very difficult to analyze the fault for dissolved gas analysis(DGA) because of the less and uncertain transformer gas information. In this paper, the Quantum Neural Network(QNN) was applied to diagnosis the transformer fault by employing the DGA data. The QNN can be used to reduce the uncertainty of pattern recognition by allocating the uncertain data to the right fault pattern quickly and rationally as its model draws on the ideas of quantum phase computing and the complementary amendment relationship exists between the hidden layer output and phase shift parameters. The validity and feasibility of the proposed method were verified by testing the real DGA data. The results of the proposed model was compared with BP neural network in dealing with the actual DGA data also.