A Machine Learning Surrogate for Component Criticality Ranking in Interdependent Power-Communication Networks
2026-07-09 • Machine Learning
Machine Learning
AI summaryⓘ
The authors looked at how power systems and communication networks depend on each other and can fail in a chain reaction. Because simulating all possible failure scenarios is very slow, they used a machine-learning model to quickly predict how bad a failure would be based on system features. Their approach closely matches the accuracy of the original complex simulator and helps identify the most critical components to protect. This method can speed up planning by quickly narrowing down which parts need detailed analysis.
Cyber-physical systemsCascading failuresPower systemsCommunication networksContingency analysisMachine learning surrogateGradient BoostingCriticality rankingInterdependencySpearman correlation
Authors
Sohini Roy, Xheni Hylviu
Abstract
Cyber-physical power systems are vulnerable to cascading failures caused by tight interdependencies between power and communication infrastructures. Evaluating these failures over large N-k contingency sets with a high-fidelity simulator is computationally prohibitive for resilience planning. Using the previously published Modified Implicative Interdependency Model (MIIM) as the ground-truth cascade simulator, this paper develops a machine-learning surrogate that predicts contingency severity from leakage-free structural features and derives a component-criticality ranking for prioritized hardening analysis. On the IEEE 118-bus system, the Gradient Boosting surrogate achieves Spearman correlations of 0.849 for per-contingency severity prediction and 0.853 for per-component criticality ranking, while remaining stable across three independently sampled datasets. MIIM-derived component criticality itself reproduces only to a Spearman of approximately 0.85 under the present sampling pipeline, and the surrogate operates at this empirical ceiling to within sampling variation. Topological centrality measures on the full interdependent network provide meaningful baselines (Spearman 0.60-0.69), and feature ablation shows that the surrogate's advantage is driven primarily by inter-layer dependency information. These results support a two-stage workflow in which the surrogate rapidly ranks candidate components and MIIM is reserved for selective verification.