Limitation of Reactance Perturbation Strategy Against False Data Injection Attacks on IoT-Based Smart Grid

Zhenyong Zhang, Bingdong Wang, Mengxiang Liu, Yan Qin, Jingpei Wang, Youliang Tian, Jianfeng Ma

Abstract: With the goal of defending against false data injection attacks (FDIAs) on state estimation (SE) of the Internet of Things (IoT)-based smart grid, recently, the reactance perturbation strategy (RPS) has been proposed by actively perturbing the branch reactances of transmission lines. Satisfied defending performance as it shows, the limitations have not been sufficiently studied by pioneer works. In this article, by exploring the vulnerability implied by the transmission network structure, we deeply investigate the limitations of RPS with both theoretical and numerical results. First, we prove that improperly selecting the branches to perturb can make RPS fail to prevent the SE from FDIAs. Second, by exploiting the properties of the system topology, we classify the branches and buses into different types and derive the limitations of RPS with analytical results. Third, an enhanced RPS is proposed to defend against FDIAs with a complete defense goal. Finally, extensive simulations are conducted in IEEE test power systems to verify the correctness of analytical results and validate the effectiveness of the enhanced RPS.