Abstract: In order to research the pattern recognition of phase resolved partial discharge (PRPD) spectrum of gas insulated switchgear (GIS) and solve the problem of low accuracy of traditional statistical parameter analysis methods, a method for pattern recognition of partial discharge PRPD spectrum in GIS based on deep residual network is proposed in this paper. Firstly, the experimental models of four typical types of partial discharge defects in GIS are set up and the experimental data are collected. Then, a conditional generative adversarial network is used to expand the data of PRPD spectrum training set. Finally, the characteristics of PRPD spectrum of each defect are extracted and classified by deep residual network. Experimental results show that the recognition accuracy of the proposed method is significantly improved compared with that of the convolutional neural network or the traditional statistical parameter analysis method. The recognition accuracy is up to 98.75%. The results of research show that the proposed method can be adopted to effectively distinguish four typical types of partial discharge defects in GIS and has a good application prospect in engineering practice.