Xing-Fan Su

Physics-Informed Spatiotemporal Deep Learning for Cable Fault Detection and Localization in Smart Power Grids

This study proposes a physics-informed spatiotemporal deep learning framework for fault detection and localization in medium-voltage cable systems that embeds telegrapher-equation residual constraints and distributed-generation boundary consistency within a physics-informed neural network to incorporate physical knowledge into the learning process.