Ranking-Based Physics-Informed Line Failure Detection in Power Grids

TL;DR

This paper introduces FIELD, a physics-informed method that improves real-time line failure detection in power grids by leveraging topology data, reducing data needs, and enhancing accuracy under extreme weather conditions.

Contribution

The paper presents a novel physics-informed failure detection approach that outperforms existing methods by integrating grid topology information to enhance detection efficiency and accuracy.

Findings

FIELD demonstrates superior detection accuracy over state-of-the-art methods.

The approach reduces sample and time complexity in failure detection.

Empirical tests show improved localization and reliability in extreme weather scenarios.

Abstract

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.