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Though recent advancements in DC microgrids are largely based on distributed control strategies to enhance reliability and scalability, the absence of a centralized controller to check the global information makes these schemes highly susceptible to cyber attacks. Since false data injection attacks (FDIAs) are considered as a prominent attack methodology in DC microgrids, prior emphasis is usually laid on compromised sensors and controllers only related to DC voltages. Hence, this paper firstly segregates the FDIAs on the output currents into destablization and deception attacks, based on the modeling of attack elements with respect to the consensus theory. Secondly, a discordant element based detection approach is designed to detect the attacked nodes accurately, using an extended analysis of the cooperative control network. A risk assessment framework for DC microgrids against cyber attacks is provided alongside all the case studies. An evaluation theory is also presented to assist the proposed detection scheme to differentiate between cyber attacks and faults. Further, the proposed detection approach is theoretically verified and validated using simulation and experimental conditions.

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It is well known that distributed control can improve the resiliency of DC microgrids against multiple link failures as compared to centralized control. However, the control layer is still vulnerable to cyber attacks. Unlike widely studied false data injection attacks (FDIAs), which involve adding false signals on top of existing ones in the controller or communication links, hijacking attacks completely replace the existing signals. As a result, the compromised agent(s) diverge from steady state owing to imbalance in the iterative rule of consensus algorithm. To detect hijacking attacks, a novel distributed screening (DS) methodology is proposed. In addition to that, a fault detection (FD) metric is provided to assist the proposed attack detection strategy in differentiating between hijacking attacks and sensor faults. This reduces the complexity of decision making in the attack mitigation approach. Further, interoperability of the proposed detection metrics allows simultaneous detection of sensor faults and hijacking attacks. The performance of the proposed detection metrics is evaluated under simulation and experimental conditions to conclude that it successfully detects the attacked agent(s) as well as sensor fault(s).

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