Physics-Informed is an approach that integrates physical laws and principles directly into the learning or modeling process. This is often done to improve the accuracy, reliability, and interpretability of models, especially in domains where physical phenomena play a crucial role, such as engineering, fluid dynamics, materials science, and structural health monitoring.

Posts

Distributed Fiber Optic Sensing for Fault Localization Caused by Fallen Tree Using Physics-informed ResNet

/in /by

Falling trees or their limbs can cause power lines to break or sag, sometimes resulting in devastating wildfires. Conventional protections such as circuit breakers, overcurrent relays and automatic circuit reclosers may clear short circuits caused by tree contact, but they may not detect cases where the conductors remain intact or a conducting path is not sufficient to create a full short circuit. In this paper, we introduce a novel, non-intrusive monitoring technique that detects and locates fallen trees, even if a short circuit is not triggered. This method employs distributed fiber optic sensing (DFOS) to detect vibrations along the power distribution line where corresponding fiber cables are installed. A physics-informed ResNet model is then utilized to interpret this information and accurately locate fallen trees, which sets it apart from traditional black-box predictions of machine learning algorithms. Our real-scale lab tests demonstrate highly accurate and reliable fallen tree detection and localization.

Read more