A Machine Learning Surrogate for Component Criticality Ranking in Interdependent Power-Communication Networks
Cyber-physical power systems are vulnerable to cascading failures caused by interdependencies between power and communication infrastructures. Because evaluating large N-k contingency sets with a high-fidelity simulator is computationally expensive, this paper develops a machine-learning surrogate using the previously published Modified Implicative Interdependency Model (MIIM) as the ground-truth cascade simulator. The surrogate predicts contingency severity from leakage-free structural features and derives an association-based component-criticality ranking for resilience screening. On the IEEE 118-bus system, Gradient Boosting achieves a held-out Spearman correlation of 0.849 for conting...
| Authors | Sohini Roy, Xheni Hylviu |
|---|---|
| Primary category | cs.LG |
| Published | 9 July 2026 |
| Last updated at source | 31 July 2026 |
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