Cyber Cascades Screening Considering the Impacts of False Data Injection Attacks

The attackers in smart grids can design false injection data attacks to intentionally cause branch overloaded tripping, which may trigger a cascading failure and thus impose substantial damages to the power grids. To address such risk, we in this paper propose an approach for screening out the most severe cascading failures triggered by potential data attacks. The proposed approach identifies these cascades and quantifies their impacts by assessing the data attacks induced branch overloads in a multistage model. The approach also includes a tool for handling the system separations during the spreading of a cascade. The simulations on the IEEE 118-bus system verify the proposed screening approaches and highlight the necessity of enhancing the defensive procedures for addressing such risk in smart grids.