Research on Fast Early Warning of False Data Injection Attack in CPS of Electric Power Communication Network

Abstract

The power grid is vulnerable to bad data interference and false data attacks, so its security is reduced. This paper focuses on the study of false data injection attacks (FDIAs), analyzes the principle of FDIAs and their impact on power systems, and studies the methods of suppressing and detecting FDIAs based on distributed state estimation and neural networks. In addition, this paper establishes a specific simulation model. Simulation results show that the proposed method can effectively identify FDIAs and correct bad data, thus further reducing the impact of FDIAs on power system state estimation. Therefore, in the follow-up, we can use this method to carry out practical research in the power communication network to further improve the security of the power communication network.