Domain-alignment multitask learning network for partial discharge condition assessment with digital twin in gas-insulated switchgear

Deep-learning-driven methods have made great progress in the condition assessment of partial discharge (PD) which including diagnosis and location in gas-insulated switchgear (GIS). However, these methods perform diagnosis and location as two separate tasks and ignore the coupling relationship. In addition, these methods all require obtaining sufficient samples to develop models, and the model becomes ineffective when there is a significant difference in sample distribution. Therefore, we propose a novel domain-alignment multitask learning network (DAMTLN) for condition assessment including diagnosis and location assisted by digital twin. Firstly, a digital virtual model is established to assist the actual condition assessment of GIS PD. Then, a novel multitask network is constructed to mine the coupling relationship between the two tasks. Finally PD condition assessment guided by a digital twin model are achieved via a combination of local-maximum-mean-discrepancy-based and adversarial -based domain adaptation, in which fine-grained information on each category is captured. Experimental results show that the proposed DAMTLN achieved a diagnostic accuracy of 98.73%, and the mean absolute error of location was 9.06 cm, which were significantly better than the results of other methods. The DAMTLN thus provides a new avenue for PD diagnosis and location driven by 'data-physics' coupling.