Artificial intelligence in power equipment fault diagnosis

An artificial neural network and expert system based diagnostic system for transformer fault diagnosis using dissolved gas-in-oil analysis (DGA) has been developed. This system takes advantage of the inherent positive features of each method and offers a better diagnostic accuracy. The knowledge base of its expert system (EPS) is derived from IEEE and IEC DGA standards and expert experiences to include as many known diagnosis rules as possible. The topology and training data set of its artificial neural network (ANN) are carefully selected to extract known as well as unknown diagnostic rules implicitly. The combination of the ANN and EPS outputs has an optimization mechanism to ensure high diagnostic accuracy. This work has been reported in the past. In this paper, the new development in fault location identification using logistic regression analysis and neural network is introduced. Test results show that it is possible not only to diagnosis and predict fault types, but also to predict the location of the fault.