Mutitask Learning Network for Partial Discharge Condition Assessment in Gas-Insulated Switchgear
Yanxin Wang, Jing Yan, Wenjie Zhang, Zhou Yang, Jianhua Wang, Yingsan Geng, Dipti Srinivasan
Abstract
Condition assessment for gas-insulated switchgear (GIS), which are crucial component of power systems, involves three interrelated aspects, i.e., partial discharge (PD) diagnosis, localization, and severity assessment. However, existing methods for GIS PD condition assessment perform these aspects as separate tasks, ignoring the mutual influence among them and leading to inferior performance. To settle the abovementioned issue, we propose a multitask learning network (MTLN) for GIS PD condition assessment. First, a multitask network was developed, taking severity assessment as the main task and diagnosis and localization as parallel auxiliary tasks. This model not only facilitates the extraction of the coupling relationship between diagnosis and localization but also furnishes pertinent feature information for severity assessment. Second, to deploy the developed model to label-free scenarios on-site, a novel subdomain adaptation is established. The process of subdomain adaptation considers the alignment of both intraclass and interclass information, incorporating a secondary filtering mechanism to mitigate the issue of feature mismatch caused by incorrect pseudo labels. Experimental results show that the proposed MTLN not only offers diagnosis and location information for severity assessment but also facilitates the exploration of the coupling relationship between diagnosis and localization, thereby enhancing the performance of GIS PD condition assessment.