A Machine Learning Surrogate for Component Criticality Ranking in Interdependent Power-Communication Networks

2026-07-09

AI Summary

This study addresses the prohibitive computational cost of high-fidelity simulations in evaluating cascading failures of power-communication coupled systems under large-scale N-k contingencies, which hinders resilience planning. To overcome this challenge, the authors propose a structure-based machine learning surrogate model that, for the first time, integrates leak-free topological centrality measures with cross-layer dependency information to rapidly predict failure severity and generate component criticality rankings. This surrogate model forms the first stage of a two-stage workflow paired with high-fidelity MIIM simulations for prioritized hardening analysis. Evaluated on the IEEE 118-bus system, the model achieves Spearman correlation coefficients of 0.849 and 0.853 for failure severity prediction and criticality ranking, respectively—significantly outperforming purely topological baselines and closely approaching the empirical upper bound of high-fidelity simulation, thereby substantially improving assessment efficiency without compromising accuracy.

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.

Problem

• interdependent networks
• cascading failures
• component criticality
• resilience planning
• N-k contingency

Innovation

• machine learning surrogate
• interdependent networks
• component criticality ranking
• cascading failures
• dependency-aware features