For diagnosing potential internal fault of power transformer

a kind of fault diagnosis model of transformer based on a multilayer perceptron (MLP) is proposed with the dissolved gas analysis (DGA) as the characteristic quantity. The fault data of transformer in actual operation is taken as the learning sample

the simulated annealing method is used to achieve connection weight optimization between the internal nodes of the MLP. The different characteristic combination is taken as the input of the MLP

the accuracy rate of the MLP diagnosis fault type is compared and analyzed. The influence of topology of the MLP and the regularization of parameters on the generalization performance of the diagnosis model is studied. The data of diagnosis model other than the training data is used to test the learned diagnosis model

and a higher diagnosis accuracy is obtained.