A Study on Partial Discharge Fault Identification in GIS Based on Swin Transformer-AFPN-LSTM Architecture

Aiming at the problem of manual feature extraction and insufficient mining of feature information for partial discharge pattern recognition under different insulation faults in GIS, a deep learning model based on phase and timing features with Swin Transformer-AFPN-LSTM architecture is proposed. Firstly, a GIS insulation fault simulation experimental platform is constructed, and the PRPD phase data and TRPD timing data under different faults are obtained; secondly, the TRPD timing data are converted into MTF; then the PRPD phase data and MTF timing data are input into the Swin Transformer-AFPN-LSTM model and other deep learning models for performance comparison. The experimental results show that the Swin Transformer-AFPN-LSTM model improves the performance by 14.09–21.23% compared with the traditional CNN model and LSTM model. Moreover, using this model to extract phase features and timing features simultaneously improves the accuracy by 10.67% and 8.66%, respectively, compared with single feature extraction, and the overall accuracy reaches 98.82%, which provides a new idea for GIS insulation fault identification.