Cyber Attack Detection and Correction Mechanisms in a Distributed DC Microgrid

DC microgrids (DCMGs) are evolving into cyber-physical systems with advanced communication networks and computational methodologies, making them sensitive to cyber-attacks. These attacks can threaten the safe operation of a system. In this article, a virtual sensor-based framework is proposed for continuously monitoring and detecting malicious activities at the hardware level of a DCMG system. An adaptive state observer with a low computational burden is designed to estimate the voltage and current of a dc–dc converter. The proposed method considers the effects of cyber-attacks by injecting false data (FD) at targeted nodes in an attempt to affect the voltage regulation and current sharing by modifying the onboard voltage and current sensors. In the presented strategy, first, a DCMG is operated and controlled without any false data injecting attacks with different load conditions. Next, the DCMG is exposed to various cyber-attacks, and the output voltages and currents of dc–dc converters are estimated. Based on the error between the estimated and actual sensed signals, the cyber-attack is detected and corrected with the proposed framework. The proposed scheme is simple in design, efficient in operation, and easily implemented in a distributed control system. The effectiveness and robustness of the proposed method are illustrated by implementing it in real-time hardware using the LabView test bench by considering four dc systems (including a solar photovoltaic system) connected in a ringlike manner communicated via an ethernet-based local area network.