Prediction and mitigation of nonlocal cascading failures using graph neural networks

• Published in: Chaos (Woodbury, N.Y.) N.Y.), 2023-01, Vol.33 (1), p.013115-013115
• Main Authors: Jhun, Bukyoung, Choi, Hoyun, Lee, Yongsun, Lee, Jongshin, Kim, Cook Hyun, Kahng, B.
• Format: Article
• Language: English
• Subjects: Avalanches; Computational efficiency; Computing time; Failure; Graph neural networks; Neural networks; Nodes
• ISSN: 1054-1500; 1089-7682
• DOI: 10.1063/5.0107420

Cascading failures in electrical power grids, comprising nodes and links, propagate nonlocally. After a local disturbance, successive resultant can be distant from the source. Since avalanche failures can propagate unexpectedly, care must be taken when formulating a mitigation strategy. Herein, we propose a strategy for mitigating such cascading failures. First, to characterize the impact of each node on the avalanche dynamics, we propose a novel measure, that of Avalanche Centrality (AC). Then, based on the ACs, nodes potentially needing reinforcement are identified and selected for mitigation. Compared with heuristic measures, AC has proven to be efficient at reducing avalanche size; however, due to nonlocal propagation, calculating ACs can be computationally burdensome. To resolve this problem, we use a graph neural network (GNN). We begin by training a GNN using a large number of small networks; then, once trained, the GNN can predict ACs efficiently in large networks and real-world topological power grids in manageable computational time. Thus, under our strategy, mitigation in large networks is achieved by reinforcing nodes with large ACs. The framework developed in this study can be implemented in other complex processes that require longer computational time to simulate large networks.