Optimal D-FACTS Placement in Moving Target Defense Against False Data Injection Attacks

Optimal D-FACTS Placement in Moving Target Defense Against False Data Injection Attacks
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DOI:
10.1109/tsg.2020.2977207
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发表时间:
2020-09
影响因子:
9.6
通讯作者:
Bo Liu;Hongyu Wu
Bo Liu;Hongyu Wu
中科院分区:
工程技术1区
文献类型:
--
作者:
Bo Liu;Hongyu Wu

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移动目标防御(MTD)是一种针对分布式柔性交流输电系统(D-FACTS)状态估计的虚假数据注入(FDI)攻击的防御策略。然而,现有的研究忽视了一个基本而关键的问题,即,D-FACTS布局,假设所有线路都配备了D-FACTS设备。为了解决这个问题,我们首先推导出一个完整的MTD的D-FACTS布局的必要条件和要求。此外,我们提出了充分的条件,使用基于图论的拓扑分析,以确保下提出的D-FACTS布局的MTD具有其复合矩阵的最大秩,这是指示MTD的有效性。基于分析条件,我们设计了D-FACTS布局算法,通过使用最少的D-FACTS设备的数量,以实现最大的MTD效率。提出了一种新的基于MTD的ACOPF模型,在该模型中,D-FACTS线路的电抗被引入作为决策变量,以找到系统损耗和MTD有效性之间的权衡。在6节点、IEEE 14节点和IEEE 118节点系统上的数值结果表明,采用D-FACTS布局算法的MTD在最大化复合矩阵秩和检测FDI攻击方面具有很好的效果。
Moving target defense (MTD) is a defense strategy to detect stealthy false data injection (FDI) attacks against the power system state estimation using distributed flexible AC transmission system (D-FACTS) devices. However, existing studies neglect to address a fundamental yet critical issue, i.e., the D-FACTS placement, by assuming that all lines are equipped with D-FACTS devices. To tackle this problem, we first derive analytical necessary conditions and requirements on the D-FACTS placement for a complete MTD. Further, we propose sufficient conditions using a graph theory-based topology analysis to ensure that the MTD under the proposed D-FACTS placement has the maximum rank of its composite matrix, which is indicative of the MTD effectiveness. Based on the analytical conditions, we design D-FACTS placement algorithms by using the minimum number of D-FACTS devices to achieve the maximum MTD effectiveness. A novel MTD-based ACOPF model, in which the reactance of D-FACTS lines is introduced as decision variables, is proposed to find a trade-off between the system loss and the MTD effectiveness. Numerical results on 6-bus, IEEE 14-bus, and IEEE 118-bus systems show the efficacy of MTDs using the proposed D-FACTS placement algorithms in maximizing the composite matrix rank and detecting FDI attacks.