Multi-source domain adaptation network for partial discharge severity assessment in gas-insulated switchgear

Abstract Gas-insulated switchgear (GIS) partial discharge (PD) severity assessment is critical for ensuring the reliable operation of GIS systems. However, existing assessment methods often overlook the long-term dependencies of historical data and fail to adequately address challenges related to limited on-site samples and variations in sample distribution. To overcome these challenges, we propose a novel multi-source domain adaptation network (MSDAN) specifically designed for on-site GIS PD severity assessment, which is the first model developed considering distribution differences in GIS PD severity assessment for different defect types. Our approach begins with the development of a feature extractor that captures both discernible PD features and long-term dependencies. We then introduce a multi-source domain adaptation strategy to mitigate distribution disparities across PD severity samples from different defect types, ensuring effective domain alignment. Additionally, we incorporate an adaptive weighted classification mechanism to accurately assess PD severity by considering the varying contributions of different defect types to the target domain task. Experimental results demonstrate that MSDAN achieves a remarkable accuracy of 95.38% in on-site GIS PD severity assessment, outperforming other benchmark models in both accuracy and robustness. This highlights the potential of MSDAN as a robust solution for real-world GIS PD severity assessment.