Pattern recognition for partial discharge diagnosis of power transformer

This paper presents a novel pattern recognition approach based on a four-layer artificial neural network (ANN) for the partial discharge (PD) diagnosis of power transformer. A precious PD detector is used to measure 3-D (φ-Q-N) signals and PD-fingerprints of four experimental models in a shielded laboratory. This work has established a database containing 160 sets of 3-D patterns and PD-fingerprints. The database is used as the training data to train a back-propagation neural network. The training-accomplished neural network can be a good diagnosis system for the practical insulation diagnosis of epoxy-resin power transformers. The proposed approach is successfully applied to practical power transformers field experiments. Experimental results demonstrated that the proposed pattern recognition approach has high recognition rate.