Study on Discharge Mode and Characteristics of Insulation Defects in GIS Equipment

Abstract

Partial discharge caused by different insulation defects has different degrees of damage to the insulation performance of gas insulating switchgear (GIS). To timely and effectively find the early insulation defects in the equipment, the insulation status is evaluated and diagnosed. Therefore, it is necessary to study the discharge mode and characteristics of insulation defects in GIS equipment. Firstly, this paper builds an equal scale GIS internal defect simulation platform in the laboratory. A variety of different insulation defects were simulated and the relevant partial discharge signals were collected. Data augmentation of the discharge pattern training set using conditional generative adversarial networks. Finally, the improved depth residual network is used to extract the discharge spectrum features of each type of defects to classify them, and the recognition accuracy of different types of defects is obtained. The experimental results show that the proposed method can effectively distinguish different typical partial discharge defect types in GIS, and has a good application prospect in engineering practice.