Work

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

Year: 2024

Type: article

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.

Source: Journal of Cyber Security and Mobility

DOI: 10.13052/jcsm2245-1439.1365

Authors: Jiankun Zhao, Kaiyue An, Xiang Wang

Institutions: Inner Mongolia Electric Power Research Institute, Inner Mongolia Electric Power (China)

Language: English

Topic: Smart Grid Security and Resilience

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: diamond

OpenAlex ID: W4404649589

FWCI: 0.315

Cites: 19

Cited by: 2

Related to: 10