Partial Discharge Diagnosis Using Semi-Supervised Learning and Complementary Labels in Gas-Insulated Switchgear

Deep neural networks have proven to be highly efficient in fault detection and classification using partial discharges (PDs) in gas-insulated switchgear (GIS). However, previous studies have not fully addressed the issue of limited labeled training data, which significantly impacts the performance of these models. Existing semi-supervised learning (SSL) approaches typically discard low-confidence pseudo-labels from unlabeled PD samples, because these unreliable labels can mislead the model. This gap in current research overlooks the potential value of low-confidence samples, which, despite their uncertain class, are unlikely to belong to the classes with the lowest probabilities. In this study, we aim to overcome this limitation by proposing a novel semi-supervised contrastive complementary learning (SCCL) method. Our SCCL approach generates a larger set of reliable negative pairs using complementary labels, allowing us to utilize the entire set of unlabeled PD samples effectively. We validate the feasibility of SCCL using phase-resolved PDs (PRPDs) and onsite noise data collected through an ultrahigh-frequency (UHF) PD measurement system. Experimental results indicate that the SCCL method achieves an impressive accuracy of 96.23% by effectively leveraging low-confidence unlabeled PD samples to improve classification performance in GIS under restricted labeling conditions.