Ranking-Based Physics-Informed Line Failure Detection in Power Grids
This work addresses real-time and accurate detection of potential line failures in power grids for mitigating extreme weather impacts, representing a domain-specific incremental improvement.
The paper tackles the problem of detecting line failures in power grids during extreme weather events by proposing a physics-informed detector (FIELD) that incorporates grid topology to reduce sample and time complexities and improve localization accuracy, demonstrating superior empirical performance compared to state-of-the-art methods.
Climate change increases the number of extreme weather events (wind and snowstorms, heavy rains, wildfires) that compromise power system reliability and lead to multiple equipment failures. Real-time and accurate detecting of potential line failures is the first step to mitigating the extreme weather impact and activating emergency controls. Power balance equations nonlinearity, increased uncertainty in generation during extreme events, and lack of grid observability compromise the efficiency of traditional data-driven failure detection methods. At the same time, modern problem-oblivious machine learning methods based on neural networks require a large amount of data to detect an accident, especially in a time-changing environment. This paper proposes a Physics-InformEd Line failure Detector (FIELD) that leverages grid topology information to reduce sample and time complexities and improve localization accuracy. Finally, we illustrate the superior empirical performance of our approach compared to state-of-the-art methods over various test cases.