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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Abstract

On the way to combating climate change, decarbonisation of electricity generation is becoming increasingly important. Worldwide targets are set for the increase of renewable power generation in electricity networks. Consequently, a secure power system that can handle the complexities resulted from the increased renewable source 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. Early detection and containment of such failures is challenging because it is combinatorial in nature and has to contend with the spatial aspects of the power system as well as the temporal evolution of failures. 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 in this research topic.