Edge-Computing-Based Knowledge Distillation and Multitask Learning for Partial Discharge Recognition

Developing an accurate partial discharge (PD) monitoring system for switchgear has attracted significant attention in recent times. Detecting and distinguishing PDs with a portable PD detector is challenging due to the inherent noise interference and the similarity among different PD signals in field conditions. In this study, we propose an innovative approach that combines knowledge distillation (KD) and multitask learning (MTL) to efficiently transfer valuable insights acquired by an advanced network to a more lightweight student network. Notably, a novel energy-adaptive sampling strategy is applied to frequency feature to represent acquired pulse waveforms efficiently. Furthermore, a novel spatial information distillation module is also proposed to enhance the knowledge transfer, thereby enhancing PD recognition efficiency and accuracy. Signals captured in substations undergo clustering through a
$k$
-means model, effectively separating PDs from noise. Using the phase resolved PD (PRPD) patterns generated from clustered signals, we train our PD classification network. With the integration of KD and MTL modules, our PD classification model outperforms the baseline model in terms of both speed and accuracy. A comprehensive assessment of each proposed module was conducted through ablation studies in our experiments. Furthermore, our constructed PD recognition dataset was used to conduct a comparative analysis against other methodologies, showcasing the superior performance of our approach.