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| Interpretable fault diagnosis for overhead lines with covered conductors: a physics-informed deep learning approach |
| Genghong Lu,Chi Wai Tsang,Ho Nam Yim,Chao Lei,Siqi Bu, Senior Member, IEEE,Winco K. C. Yung, Senior Member, IEEE,Michael Pecht, Fellow, IEEE |
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| Abstract: |
| Partial discharge (PD) activity is an indicator of insulation deterioration and by extension, the reliability of power lines. Existing data-driven methods, while helpful, treat PD detection as a binary classification problem, thereby failing to provide physical information (e.g., filter PD pulse), and often provide results that contradict physical knowledge. To tackle this challenge, this paper develops a physics-informed temporal convolutional network (PITCN) for PD diagnosis (i.e., PD detection and PD pulse filtering). During training, physical knowledge of the background noise and PD pulse identification is integrated into a learning model. Once the model is trained, the PITCN can automatically detect PD activity from time-series voltage signals with different background noises and filter PD pulses. Experimental results demonstrate that the developed PITCN outper-forms the rest of the data-driven methods implemented, and in particular, the Matthews correlation coefficient of PITCN surpasses the conventional temporal convolutional network by 0.21. |
| Key words: Intelligent fault diagnostics, interpretable detection, partial discharges, physical knowledge, power line protection. |
| DOI：10.23919/PCMP.2023.000159 |
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| Fund:This work is supported by the Centre for Advances in Reliability and Safety (CAiRS) admitted under AIR@InnoHK Research Cluster. |
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