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Defending Against False Data Injection Attacks on Power System State Estimation

机译:防止错误数据注入攻击对电力系统状态的估计

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This paper investigates the problem of defending against false data injection (FDI) attacks on power system state estimation. Although many research works have been previously reported on addressing the same problem, most of them made a very strong assumption that some meter measurements can be absolutely protected. To address the problem practically, a reasonable approach is to assume whether or not a meter measurement could be compromised by an adversary does depend on the defense budget deployed by the defender on the meter. From this perspective, our contributions focus on designing the least-budget defense strategy to protect power systems against FDI attacks. In addition, we also extend to investigate choosing which meters to be protected and determining how much defense budget to be deployed on each of these meters. We further formulate the meter selection problem as a mixed integer nonlinear programming problem, which can be efficiently tackled by Benders’ decomposition. Finally, extensive simulations are conducted on IEEE test power systems to demonstrate the advantages of the proposed approach in terms of computing time and solution quality, especially for large-scale power systems.
机译:本文研究了针对电力系统状态估计的防御虚假数据注入(FDI)攻击的问题。尽管先前已经报道过许多针对解决同一问题的研究工作,但大多数研究人员都非常有力地认为某些电表测量可以得到绝对保护。为了在实践中解决该问题,一种合理的方法是假设对手是否会损害仪表的测量结果,这确实取决于防御者在仪表上部署的国防预算。从这个角度来看,我们的贡献集中在设计最低预算的防御策略,以保护电力系统免受FDI攻击。此外,我们还扩展了调查范围,以选择要保护的电表,并确定在这些电表中的每个电表上部署多少国防预算。我们进一步将仪表选择问题表述为混合整数非线性规划问题,可以通过Benders的分解有效地解决它。最后,在IEEE测试电源系统上进行了广泛的仿真,以证明该方法在计算时间和解决方案质量方面的优势,特别是对于大型电源系统。

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