Power transformer fault diagnosis based on dissolved gas analysis by artificial neural network

This paper presents an intelligent fault classification approach to power transformer dissolved gas analysis (DGA). Artificial neural network (ANN) is powerful for the problem with small sampling and high dimension. ANN is applied to establish the power transformers faults classification and to choose the most appropriate gas signature between the DGA traditional methods and a novel extension method. The experimental data from Tunisian Company of Electricity and Gas (STEG) is used to illustrate the performance of proposed ANN models. Then, the MLP and RBF classifier are trained with the training samples. Finally, the normal state and the six fault types of transformers are identified by the trained classifier. In comparison to the results obtained from the ANN, the proposed DGA method has been shown to possess superior performance in identifying the transformer fault type. The test results indicate that the ANN approach can significantly improve the diagnosis accuracies for power transformer fault classification.