Cascading Failure Prediction via Causal Inference
Shiuli Subhra Ghosh, Anmol Dwivedi, Ali Tajer, Kyongmin Yeo, Wesley M. Gifford
Abstract
Causal inference provides an analytical framework to identify and quantify cause-and-effect relationships among a network of interacting agents. This article offers a novel framework for analyzing cascading failures in power transmission networks. This framework generates a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">directed</i> latent graph in which the nodes represent the transmission lines and the directed edges encode the cause-effect relationships. This graph has a structure distinct from the system's topology, signifying the intricate fact that both local and non-local interdependencies exist among transmission lines, which are more general than only the local interdependencies that topological graphs can present. This article formalizes a causal inference framework for predicting how an emerging anomaly propagates throughout the system. Using this framework, two algorithms are designed, providing an analytical framework to identify the most likely and most costly cascading scenarios. The framework's effectiveness is evaluated and compared to the pertinent literature on the IEEE 14-bus, 39-bus, and 118-bus systems.