Predicting Cascading Failures in Power Systems using Graph Convolutional Networks (Proposals Track)

Tabia Ahmad (University of Strathclyde); Yongli Zhu (Texas A&M Universersity); Panagiotis Papadopoulos (University of Strathclyde)

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Worldwide targets are set for the increase of renewable power generation in electricity networks on the way to combat climate change. Consequently, a secure power system that can handle the complexities resulted from the increased renewable power integration is crucial. One particular complexity is the possibility of cascading failures — a quick succession of multiple component failures that takes down the system and might also lead to a blackout. Viewing the prediction of cascading failures as a binary classification task, we explore the efficacy of Graph Convolution Networks (GCNs), to detect the early onset of a cascading failure. We perform experiments based on simulated data from a benchmark IEEE test system. Our preliminary findings show that GCNs achieve higher accuracy scores than other baselines which bodes well for detecting cascading failures. It also motivates a more comprehensive study of graph-based deep learning techniques for the current problem.

Recorded Talk (direct link)