A Class Alignment Multisource Domain Adaptation for Partial Discharge Condition Assessment With Unknown Faults in GIS

Abstract

Recently, domain adaptation has emerged as a powerful technique for on-site partial discharge (PD) condition assessment in gas-insulated switchgear (GIS). However, most existing methods face three major challenges: 1) relying on a single source domain for model development poses difficulties in effectively utilizing source domain samples with distribution differences; 2) limited condition assessment for unknown fault samples on-site, which faces distributional differences between multiple source domains; and 3) handling only a single task, which makes it challenging to generalize to multiple tasks simultaneously. To address these concerns, we propose a class alignment multisource domain adaptation network (CLMSDAN) for GIS PD condition assessment with unknown faults. First, a diversity feature extractor is developed to extract diverse features while addressing the negative transfer issue caused by knowledge differences by mining both interdomain and intradomain features, thus enabling the transfer of rich knowledge at multiple levels. Second, a novel multisource domain adaptation approach is employed from multiple perspectives to align distribution and distinguish between shared and unknown classes. Finally, a multiclassifier complementary strategy is introduced to recognize unknown faults, which automatically filters out source domain irrelevant class samples while distinguishing the contributions of different source domains to the target task. Experimental results show that CLMSDAN achieves 94.86% accuracy in diagnosis and 93.38% in severity assessment, outperforming baseline methods by over 10% in both tasks. This highlights its superior generalization and robustness across varying conditions and noise levels.