AI 中文总结
研究相互依赖的电力通信网络中组件关键性排序问题，基于改进隐含相互依赖模型开发机器学习代理模型，能从结构特征预测故障严重性并排序，在IEEE 118母线系统上表现良好，支持两阶段工作流程。
AI 中文摘要
网络物理电力系统易受电力与通信基础设施间紧密相互依赖导致的级联故障影响。用高保真模拟器评估大型N-k故障集的故障在弹性规划中计算成本过高。本文以先前发表的改进隐含相互依赖模型（MIIM）作为真实级联模拟器，开发了一种机器学习代理模型，可从无泄漏结构特征预测故障严重性并得出组件关键性排序用于优先强化分析。在IEEE 118母线系统上，梯度提升代理模型在每次故障严重性预测中Spearman相关性达0.849，在每个组件关键性排序中达0.853，且在三个独立采样数据集上保持稳定。MIIM得出的组件关键性在当前采样管道下Spearman相关性仅约0.85，代理模型在此经验上限内运行。全相互依赖网络上的拓扑中心性度量提供了有意义的基线（Spearman相关性0.60 - 0.69），特征消融表明代理模型的优势主要由层间依赖信息驱动。这些结果支持两阶段工作流程，即代理模型快速对候选组件排序，MIIM用于选择性验证。
英文摘要
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 contingency-severity ranking. Using five-fold out-of-fold predictions, the resulting component ranking achieves a Spearman correlation of 0.838 with the MIIM-derived ranking and closely approaches the observed cross-sample reproducibility level. Feature-ablation results show that inter-layer dependency features drive most of the surrogate's advantage, while end-to-end screening is approximately 158x faster than direct MIIM evaluation. The results support a two-stage workflow in which the surrogate screens candidate contingencies and components, and MIIM provides selective verification rather than directly identifying optimal hardening actions.
CommentsAccepted for publication in 2026 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm): Workshop on Cyber-Physical Power System Resilience: Challenges and Emerging Solutions
