Cyber–physical collaborative control for DC microgrid clusters under joint cyber-attacks

DC microgrid (DCMG) clusters, as deeply integrated cyber–physical systems (CPSs), are vulnerable to cyber-attacks like false data-injection attacks (FDIAs) and denial-of-service (DoS) attacks. This article proposes a cyber–physical collaborative control method, against the joint attacks mentioned above. The main contribution of this work is as follows: firstly, a collaborative control architecture based on multiagent systems (MASs) is constructed for the CPS; secondly, in the physical layer, a hierarchical control framework for DCMG agents is adopted, including primary, secondary, and tertiary control layers. The secondary and tertiary control layers can adjust the scheme of power flow management and frequency/voltage control, through the interaction information between agents; thirdly, in the cyber layer, an active defense strategy based on layered event-triggered protocol has been proposed: (1) according to the tampering of transmission data/information caused by FDIA, a support vector machine (SVM) based prediction learning compensation method is employed, to ensure the effectiveness of energy management and voltage/frequency control. (2) according to the transmission path interruption caused by DoS attack, the optimal path selection method and path reconstruction method are proposed to maintain the transmission of power flow management information and control information. Simulation results are presented to illustrate the effectiveness of real-time collaborative control for the proposed control method. •A collaborative control architecture based on multiagent systems (MASs) is constructed for the CPS. The physical layer and the cyber layer are both constructed.•In the physical layer, a hierarchical control framework for DCMG agents is adopted, including primary, secondary, and tertiary control layers. The secondary and tertiary control layers can adjust the scheme of power flow management and frequency/voltage control, through the interaction information between agents.•In the cyber layer, an active defense strategy based on layered event-triggered protocol has been proposed: (1) targeting transmission data/information tampering caused by FDIA, a SVM based prediction learning compensation method is employed, to ensure the effectiveness of energy management and voltage/frequency control. (2) targeting transmission path interruption caused by DoS attack, the optimal path selection method and path reconstruction method are proposed to maintain the transmi