A novel adaptive capsule network with dual-branch feature extraction for multi-source partial discharge diagnosis in gas-insulated switchgear

作者：Jing Yan, Yanxin Wang, Qianzhen Jing, Jianhua Wang, Zhiyuan Liu, Yingsan Geng · 发表于：International Journal of Electrical Power & Energy Systems · 年份：2025 · DOI：10.1016/j.ijepes.2025.111369 · 被引用次数：2 · 研究领域：High voltage insulation and dielectric phenomena、Power Transformer Diagnostics and Insulation、Thermal Analysis in Power Transmission

• A novel ACN is proposed for accurate multi-source GIS PD diagnosis in GIS. • A dual-branch feature extraction module is designed to capture local and global PD features. • An adaptive capsule is introduced to improve classification robustness via dynamic routing. • A multi-label classification module is incorporated to support concurrent PD type prediction. • The method achieves 96.68 % accuracy for multi-source PD in noisy, small-sample scenarios. Although state-of-the-art artificial intelligence models have achieved remarkable performance in partial discharge (PD) diagnosis for gas-insulated switchgear (GIS), accurately and robustly identifying multi-source PDs remains challenging due to the complex coupling of signal patterns and noise. To address these limitations, this paper proposes a novel adaptive capsule network (ACN) featuring a dual-branch feature extraction architecture for GIS multi-source PD diagnosis. First, a dual-branch module employing U-Net and U-Transformer in parallel is developed to capture both fine-grained local details and long-range global dependencies of PD signals, with the U-Net structure further enhancing noise robustness through effective suppression. Second, an adaptive capsule structure is then introduced, where a dynamic routing algorithm models interactions among primary capsules, computes coupling coefficients, and adaptively aggregates semantically similar information into higher-level capsules to improve feature discriminability. Finall...