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首页> 外文期刊>IEEE Transactions on Automatic Control >Power Grid AC-Based State Estimation: Vulnerability Analysis Against Cyber Attacks
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Power Grid AC-Based State Estimation: Vulnerability Analysis Against Cyber Attacks

机译：电网基于交流的状态估计：针对网络攻击的漏洞分析

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摘要

To ensure grid efficiency and reliability, power system operators continuously monitor the operational characteristics of the grid through a critical process called state estimation (SE), which performs the task by filtering and fusing various measurements collected from grid sensors. This study analyzes the vulnerability of the key operation module, namely ac-based SE, against potential cyber attacks on data integrity, also known as false data injection attack (FDIA). A general form of FDIA can be formulated as an optimization problem, whose objective is to find a stealthy and sparse data injection vector on the sensor measurements with the aim of making the state estimate spurious and misleading. Due to the nonlinear ac measurement model and the cardinality constraint, the problem includes both continuous and discrete nonlinearities. To solve the FDIA problem efficiently, we propose a novel convexification framework based on semidefinite programming (SDP). By analyzing a globally optimal SDP solution, we delineate the "attackable region" for any given set of measurement types and grid topology, where the spurious state can be falsified by FDIA. Furthermore, we prove that the attack is stealthy and sparse, and derive performance bounds. Simulation results on various IEEE test cases indicate the efficacy of the proposed convexification approach. From the grid protection point of view, the results of this study can be used to design a security metric for the current practice against cyber attacks, redesign the bad data detection scheme, and inform proposals of grid hardening. From a theoretical point of view, the proposed framework can be used for other nonconvex problems in power systems and beyond.
机译：为了确保电网效率和可靠性，电力系统运营商通过称为状态估计（SE）的关键过程连续监视电网的运行特性，该过程通过过滤和融合从电网传感器收集的各种测量值来执行任务。这项研究分析了关键操作模块（即基于AC的SE）针对数据完整性的潜在网络攻击（也称为错误数据注入攻击（FDIA））的脆弱性。可以将FDIA的一般形式表述为优化问题，其目的是在传感器测量值上找到隐秘且稀疏的数据注入向量，以使状态估计变得虚假和误导。由于非线性交流测量模型和基数约束，该问题包括连续和离散非线性。为了有效地解决FDIA问题，我们提出了一种基于半定规划（SDP）的新型凸框架。通过分析全局最优的SDP解决方案，我们为任意给定的一组测量类型和网格拓扑划定了“可攻击区域”，在这种情况下，FDIA可以伪造状态。此外，我们证明了攻击是隐秘且稀疏的，并得出性能界限。在各种IEEE测试案例上的仿真结果表明了所提出的凸化方法的有效性。从网格保护的角度来看，本研究的结果可用于针对当前针对网络攻击的实践设计安全性度量标准，重新设计不良数据检测方案并为网格硬化建议提供依据。从理论上讲，提出的框架可以用于电力系统及其他系统中的其他非凸问题。

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