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arXiv 2003.08715eess.SP
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电力系统不可观测虚假数据注入攻击的结构约束识别方法

Structural-constrained Methods for the Identification of Unobservable False Data Injection Attacks in Power Systems

Gal Morgenstern, Tirza Routtenberg

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摘要：

电力系统功能性是基于电力系统状态估计（PSSE）来确定的。因此，PSSE的腐败可能会导致严重的后果，例如财务损失、维护损坏和电力分配中断。经典的坏数据检测（BDD）方法，以确保PSSE的可靠性，无法检测精心设计的攻击，称为不可观察的虚假数据注入（FDI）攻击。在本文中，我们开发了新的结构约束的方法来检测不可观察的FDI攻击，识别受攻击的巴士的位置，并PSSE下存在这样的攻击。所提出的方法基于对攻击和系统负载制定结构性、稀疏约束。首先，我们利用这些限制，以组成一个适当的模型选择问题。然后，我们开发了相关的广义信息准则（GIC）的这个问题。然而，对于大型网络，GIC方法的计算复杂度随着网络规模呈指数增长。因此，基于所提出的结构和稀疏约束，我们开发了两种新的低复杂度的方法来识别不可观测的FDI攻击：1）修改的国家的最先进的正交匹配追踪（OMP）;和2）的方法，利用图马尔可夫属性在电力系统，即在系统的总线上的功率数据之间的第二邻居关系。在IEEE-30节点测试系统上对该方法的性能进行了评估。

英文摘要：

Power system functionality is determined on the basis of the power system state estimation (PSSE). Thus, corruption of the PSSE may lead to severe consequences, such as financial losses, maintenance damage, and disruptions in electricity distribution. Classical bad data detection (BDD) methods, developed to ensure PSSE reliability, are unable to detect well-designed attacks, named unobservable false data injection (FDI) attacks. In this paper, we develop novel structural-constrained methods for the detection of unobservable FDI attacks, the identification of the attacked buses' locations, and PSSE under the presence of such attacks. The proposed methods are based on formulating structural, sparse constraints on both the attack and the system loads. First, we exploit these constraints in order to compose an appropriate model selection problem. Then, we develop the associated generalized information criterion (GIC) for this problem. However, for large networks, the GIC method's computational complexity grows exponentially with the network size. Thus, based on the proposed structural and sparse constraints, we develop two novel low-complexity methods for unobservable FDI attack identification: 1) a modification of the state-of-the-art orthogonal matching pursuit (OMP); and 2) a method that utilizes the graph Markovian property in power systems, i.e. the second-neighbor relationship between the power data at the system's buses. The methods' performance is evaluated on a IEEE-30 bus test case system.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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