Small vulnerable sets determine large network cascades in power grids
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Abstract (arXiv)
The understanding of cascading failures in complex systems has been hindered by the lack of realistic large-scale modeling and analysis that can account for variable system conditions. Here, using the North American power grid, we identify, quantify, and analyze the set of network components that are vulnerable to cascading failures under any out of multiple conditions. We show that the vulnerable set consists of a small but topologically central portion of the network and that large cascades are disproportionately more likely to be triggered by initial failures close to this set. These results elucidate aspects of the origins and causes of cascading failures relevant for grid design and operation, and demonstrate vulnerability analysis methods that are applicable to a wider class of cascade-prone networks.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Apr 2018.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 2; 2024: 0; 2025: 1; 2026: 1.
7 papers.
- 2021Machine learning dismantling and early-warning signals of disintegration in complex systemscited by 14
- 2021Power-grid stability predictions using transferable machine learningcited by 6
- 2022Predicting Dynamic Stability from Static Features in Power Grid Models using Machine Learningcited by 4
- 2023Inferring Attracting Basins of Power System with Machine Learningcited by 4
- 2025A Criminology of Machinescited by 1
- 2023Questions of science: chatting with ChatGPT about complex systemscited by 0
- 2026PowerZooJax: A JAX-based Power System Benchmark for Reinforcement Learningcited by 0
- 2026PowerZooJax: A JAX-based Power System Benchmark for Reinforcement Learning28 Sep 2026
- 2025A Criminology of Machines4 Nov 2025
- 2023Inferring Attracting Basins of Power System with Machine Learning20 May 2023
- 2023Questions of science: chatting with ChatGPT about complex systems29 Mar 2023
- 2022Predicting Dynamic Stability from Static Features in Power Grid Models using Machine Learning17 Oct 2022
- 2021Power-grid stability predictions using transferable machine learning17 May 2021
- 2021Machine learning dismantling and early-warning signals of disintegration in complex systems7 Jan 2021