Multi-Task Learning Network-Based Condition Assessment of GIS Partial Discharge: Diagnosis and Severity Evaluation

Current methods for assessing the state of partial discharge (PD) in gas-insulated switchgear (GIS) often treat the diagnosis and severity evaluation tasks independently, limiting model performance and ignoring defect types in the PD development stages. To address these issues, this paper proposed a novel multi-task network for the condition evaluation of PD. Firstly, a lightweight attention combined with an expert network module is introduced to explore the correlations between different tasks while retaining their unique features. Secondly, we incorporate a CNN-Transformer module to capture both local and global features of GIS PD signals. Lastly, we implement a dynamic weight averaging method for the multi-task network, which adaptively adjusts the loss weights via loss variation rate of each sub-task. Experimental results demonstrated the multi-task network significantly improves the GIS state assessment performance for each sub-task, advancing the development stage evaluation of PD for various insulation defects.