Cascading Blackout Severity Prediction with Statistically-Augmented Graph Neural Networks

arXiv:2403.15363v1 Announce Type: cross
Abstract: Higher variability in grid conditions, resulting from growing renewable penetration and increased incidence of extreme weather events, has increased the difficulty of screening for scenarios that may lead to catastrophic cascading failures. Traditional power-flow-based tools for assessing cascading blackout risk are too slow to properly explore the space of possible failures and load/generation patterns. We add to the growing literature of faster graph-neural-network (GNN)-based techniques, developing two novel techniques for the estimation of blackout magnitude …

abstract arxiv blackout cs.lg cs.sy eess.sy events explore flow graph graph neural networks grid networks neural networks power prediction renewable risk screening tools type weather