智能电网广域控制系统的攻击韧性分析

中文摘要 AI 辅助

广域监测、保护与控制（WAMPAC）系统可阻尼大型互联电网中的区间低频振荡，但其对经由广域网络传输的同步相量测量单元（PMU）数据的依赖使其易受虚假数据注入（FDI）攻击。本文提出一种攻击韧性分析框架，该框架对广域阻尼回路、自动发电控制、调速器及励磁系统的耦合动力学进行形式化建模，并将最优隐蔽FDI攻击表述为混合整数线性规划（MILP）问题。异常检测模型（ADM）作为可替换的约束集引入：或为静态坏数据检测（BDD）规则，或为从正常运行中学习、并校准至常见误报率的边界。在IEEE 39节点和118节点系统上，以OPAL-RT硬件在环测试平台验证的参考动力学为基准，具有广域访问权限的最优攻击达到良性目标值的3.3倍和8.1倍，且达到0.5Hz频率偏移的速度比仅限自动发电控制的攻击快两倍以上。学习边界将攻击目标降低15.5%至55.0%，并在所有配置中防止过频继电器跳闸，但每种情况下仍存在可行的隐蔽攻击，且残余风险随学习边界的宽度而非检测器类型而变化。

英文摘要

Wide-area monitoring, protection, and control (WAMPAC) systems damp inter-area oscillations in large interconnected grids, but their reliance on synchronized PMU measurements carried over wide-area networks exposes them to false data injection (FDI) attacks. This paper presents an attack-resiliency analytics framework that formally models the coupled dynamics of the wide-area damping loop, automatic generation control, and the governor and excitation systems, and formulates the optimal stealthy FDI attack as a mixed-integer linear program (MILP). The anomaly detection model (ADM) enters as a replaceable constraint set: either a static bad-data detection (BDD) rule or a boundary learned from benign operation calibrated to a common false-positive rate. On the IEEE 39 and 118 bus systems, with reference dynamics validated on an OPAL-RT hardware-in-the-loop testbed, the optimal attack with wide-area access attains 3.3 and 8.1 times the benign objective and reaches a 0.5Hz frequency excursion more than twice as fast as an attack confined to automatic generation control. Learned boundaries reduce the attack objective by 15.5 55.0% and prevent over-frequency relay trips in all configurations, with feasible stealthy attacks remaining in every case, and residual risk tracks the width of the learned boundary rather than the detector family.

发表机构

• Florida International University（佛罗里达国际大学）
• Northern Illinois University（北伊利诺伊大学）

机构由 AI 辅助整理，请以论文原文为准。