AI 中文总结
针对现有节点关键性识别方法未考虑对抗性影响的问题，提出受对抗信息驱动的框架，结合结构化攻击生成机制与联盟边际贡献评估，在IEEE 14母线系统上验证其能更精准识别关键节点。
AI 中文摘要
电网状态估计依赖于传感器测量值，在网络物理环境中这些测量值日益易受对抗性破坏，可能导致系统观测值出现显著偏差。这促使人们需要识别关键测量节点，这些节点被破坏后会对系统层面造成最严重的影响。然而，现有的节点关键性方法主要依赖结构分析或稳态分析，并未明确考虑对抗性对系统行为的影响。为解决这一差距，本文提出一种用于识别线性化电网中关键测量节点的受对抗信息驱动的框架。在该框架内，开发了一种结构化攻击生成机制，以针对H∞鲁棒状态估计器构建隐蔽且有效的虚假数据注入攻击（FDIAs）。随后，通过对规定的容许节点集进行置换采样，估计被破坏传感器子集基于联盟的边际贡献，以此评估节点关键性，并将得到的重要性分数映射到对应的物理母线。在IEEE 14母线系统上的仿真结果表明，与随机选择的节点相比，受对抗识别的节点会引发频率、电压相角和净功率出现更大偏差，证明了所提框架的有效性。
英文摘要
Power grid state estimation relies on sensor measurements that are increasingly vulnerable to adversarial corruption in cyberphysical environments, potentially leading to significant deviations in system observations. This motivates the need to identify critical measurement nodes whose compromise results in the most severe system-level impact. However, existing node criticality methods primarily rely on structural or steady-state analyses and do not explicitly account for adversarial effects on system behavior. To address this gap, this paper proposes an adversarially informed framework for identifying critical measurement nodes in linearized power systems. Within this framework, a structured attack generation mechanism is developed to construct stealthy and effective false data injection attacks (FDIAs) against an H-infinity resilient state estimator. Node criticality is then evaluated using coalition-based marginal contributions of compromised sensor subsets, estimated via permutation sampling over a prescribed set of admissible nodes, with the resulting importance scores mapped to the corresponding physical buses. Simulation results on the IEEE 14-bus system show that adversarially identified nodes induce larger deviations in frequency, voltage angle, and net power compared to randomly selected nodes, demonstrating the effectiveness of the proposed framework.
