Adopting integration of multispectral time resolved analysis and graph-based deep learning method in partial discharge type identification

Abstract

Partial discharge (PD) is treated as one of the major threats for gas insulated switchgear (GIS). By using the new generation multispectral detection sensor named as SiPM-based multispectral discharge sensor (SMDS), the time resolved partial discharge with multispectral information (named as MTRPD) for creeping discharge, suspension discharge and tip discharge, respectively. It indicates that the MTRPD for the specific discharge defect perform unique spectral fingerprints in discharge mode. Based on the graph characteristics of MTRPD, we introduced the convolution neural network (CNN) to implement PD type identification whose overall accuracy of Δqi-Δqi+1 and Δti-Δti+1 were 99.7% and 98.9%, respectively. This paper provides a new technique tool for fine diagnosis of PD independent of phase analysis.