多任务元学习网络的气体绝缘组合电器局部放电同时诊断与定位

To address the issue where gas-insulated switchgear (GIS) partial discharge diagnosis and localization are treated as separate tasks without considering their connection and in response to challenging deployment and generalization to on-site small sample scenarios, this paper introduces a multi-task meta-learning network to simultaneously achieve GIS partial discharge diagnosis and localization in on-site small sample scenarios. Initially, a multi-task network is developed to fully explore the correlation between the two tasks while preserving their differential characteristics. An attention mechanism is introduced at specific task layers to select important features from shallow to deep levels for each task, ensuring the quality of differentiated extraction of features for each task. Subsequently, a mcta-training method is adopted for model training to deploy the developed multi-task network in on-site small sample scenarios. Fine-tuning is performed using a small amount of data from the target task in the meta-testing stage, and GIS partial discharge diagnosis and location with small samples arc enabled. Finally, the model performance is verified using field samples. Experimental results demonstrate that the multi-task meta-learning network proposed in this paper shows an accuracy of 94. 53% in GIS partial discharge diagnosis, with an average error in the location of 10. 78 cm and a root mean square error of 12. 97 cm. Exhibiting superior performance relative to the single-task network and other models, the proposed multi-task meta-learning network presents a novel solution for GIS partial discharge diagnosis and location.