Interpretable Fault Diagnosis for Overhead Lines with Covered Conductors: A Physics-Informed Deep Learning Approach
Gang Lu, Chi Wai Tsang, Ho Bin Yim, Chao Lei, Siqi Bu, Winco K. C. Yung, Michael Pecht
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.