Modeling and Recognition of Latent False Data Injection Attacks on Distributed Cluster Control of Distribution Network

The cyber–physical deep integration in distribution networks containing distributed generators (DGs) enables false data injection attacks (FDIAs) through data tampering in monitoring systems, posing cross-domain threats to power system security. Effective FDIA identification methods remain unavailable for distribution networks containing DGs under distributed cluster control. The cyber–physical interaction characteristics are systematically analyzed in the cyber–physical system (CPS) of distribution networks containing DGs under distributed cluster control. A latent FDIA model is established with a specific attack penetration pattern being revealed. The operational impact mechanism of latent FDIAs on distributed cluster control is theoretically elucidated. A synchronous detection signal-based localization methodology is developed for latent FDIAs, coupled with an attack signal computation algorithm. These innovations enable dynamic attack identification and proactive signal isolation, thereby facilitating subsequent defense operations by system operators. Simulation results confirm the effectiveness of the proposed dynamic identification method for latent FDIAs in distribution networks containing DGs.