Partial discharge（PD）characteristics caused by internal insulation defects of gas insulated switchgear（GIS）are complicated

and traditional partial discharge pattern recognition method has strong subjectivity on featureselection

so the accuracy and robustness of its classification on partial discharge pattern are unsatisfactory. In re-sponse to this problem

the PRPD data of partial discharge is transformed into a partial discharge grayscale image

and a self-adaptive feature extraction of the partial discharge grayscale image is constructed by a network of deep re-siduals

in order to deeply extract the different partial discharge patterns in the discharge grayscale image and real-ize the recognition of partial discharge pattern. In fact

experiments show that the method proposed in this paper has high accuracy and robustness to the recognition of partial discharge types at the same time.