Detection and Mitigation via Alternative Data for False Data Injection Attacks in DC Microgrid Cluster

Abstract

DC microgrid clusters (DCMGCs), as deeply integrated cyber-physical systems, are formed by interconnection of multiple DC microgrids, and use distributed control to achieve power distribution with high reliability and scalability, and further reflect advantages of distributed energy resources-based generations. However, sharing of information among control agents by distributed manner in the DCMGCs renders the systems vulnerable to cyber-attacks. Among various cyber-attacks, false data injection attacks (FDIAs) can be carefully designed as stealth attacks, which can cause errors in the power management of DC-MGCs without manifestation of instability phenomena and even mislead existing detection methods to make incorrect judgments. To address this issue, this paper presents an alternative data-based strategy to detect FDIAs and mitigate the impact of the attacks in cyber network of DCMGCs. The classification conditions of FDIAs are discussed according to the different responses of DC-MGCs to the attacks. Furthermore, the core detection problem is transformed into identifying whether the system outputs match by selecting alternative communication data to circumvent complex modeling. Finally, hardware-in-the-loop experimental results on the dSPACE™ MicroLabBox platform with universal digital signal processing (DSP) controllers validate the proposed strategy.