Abstract: The traditional switchgear partial discharge pattern recognition methods lack certain generalization performance and have low recognition accuracy, so it is hard to apply them in practical engineering. A method for switchgear partial discharge pattern recognition based on residual convolutional neural network is proposed. A residual module is added to the network to solve the degradation problem after accuracy saturation caused by the deepening of network layer number, and the shallow and deep feature fusion learning of switchgear partial discharge data is integrated to achieve the pattern recognition. Based on the partial discharge simulation experiments of different insulation defect categories of switchgears and the field testing of distribution stations, a sample database of switchgear partial discharge data is constructed, and experimental analysis is conducted. The experimental results show that the recognition correct rate of the proposed method reaches 96.06%, at least 20.22% higher than that of the traditional recognition methods, and the recognition rate can be improved more with the increase of the number of samples in the training set. Through integrated use of the feature layer fusion module and residual module, the proposed model is significantly improved in the generalization performance and is more suitable for practical engineering.

Key words: convolutional neural network, residual module, feature layer fusion, partial discharge, pattern recognition