Artificial recognition system for defective types of transformers by acoustic emission

Abstract

An artificial recognition system of defective types for epoxy-resin transformers through acoustic emission (AE) from partial discharge (PD) experiment is proposed. PD detection is an efficient diagnosis method to prevent the failure of electric equipments arising from degrading insulation. However, most of the PD detection methods could be performed only at the shutdown period of equipments. By using AE, the online and real-time detection with defective types could be easily reached. Therefore, in this paper a series of high voltage tests were conducted on pre-faulty transformers to collect the AE signals for recognition system needed. The selected AE features instead of waveform are then extracted from these experimental AE signals for the input characteristic of recognition system. According to these features, effective identification of their defective types can be done using the proposed recognition system that combined particle swarm optimization with an artificial neural network. To demonstrate the effectiveness and feasibility of the proposed approach, the artificial recognition system is applied on both noisy and noiseless circumstances. The experiment showed encouraging results that even with 30% noise per discharge count, an 80% successful recognition rate can still be achieved.