Predicting cascading failures in power systems using graph convolutional networks

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Ahmad, Tabia and Zhu, Yongli and Papadopoulos, Panagiotis (2021) Predicting cascading failures in power systems using graph convolutional networks. In: NeurIPS 2021 Workshop on Tackling Climate Change with Machine Learning, 2021-12-13 - 2021-12-14. (https://www.climatechange.ai/papers/neurips2021/76)

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Abstract

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.

ORCID iDs

Ahmad, Tabia, Zhu, Yongli and Papadopoulos, Panagiotis ORCID logoORCID: https://orcid.org/0000-0001-7343-2590;
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