Semi-Supervised Learning-Based Partial Discharge Diagnosis in Gas-Insulated Switchgear

Effective monitoring and diagnosis of partial discharge (PD) in power equipment are crucial for maintenance, particularly given the expectations of significant increases in energy generation and consumption. Although deep neural networks have been widely applied in PD fault detection and classification, their performance is hindered by insufficient labeled data available for power equipment. This study proposes a semi-supervised learning (SSL) method to address the scarcity of labeled training data for PD classification in gas-insulated switchgear (GIS). The proposed SSL was validated based on phase-resolved PD and on-site noise using an ultra-high frequency (UHF) PD measurement system. Experimental results show that the proposed SSL achieves a high classification accuracy of 94.59% by effectively utilizing unlabeled data to enhance classification performance in GIS.