Small-Sample GIS Partial Discharge-Type Identification Method Based on Fusion of 1-D AT-DRSN and IDRN Models

Abstract

Different types of partial discharges (PDs) in gas-insulated switchgear (GIS) cause different degrees of GIS insulation damage, and correct PD identification is critical to GIS insulation status. This article proposes a multimodel fusion PD pattern recognition method based on 1-D adaptive transfer deep residual shrinkage network (AT-DRSN) and improved deep residual network (IDRN), which fully utilizes PD time-domain waveform images generated from field inspection. First, the finite-difference time-domain (FDTD) method and mathematical models are used to simulate GIS PDs, and the time-domain waveform image datasets of four typical PD defects are established. Second, the network model is built based on time-domain feature sequences and time-order feature parameters, respectively, and effectively combined with the transfer learning method, and finally, the AT-DRSN and IDRN models are weighted and fused for PD pattern recognition. The results show that the model proposed in this article can effectively achieve high-precision diagnosis of small-sample GIS PDs, with a recognition accuracy of 98.3%, and the accuracy under strong noise is still greater than 95%, which has higher accuracy and anti-interference performance compared with other methods and is of reference value for small-sample PD recognition in the field.