Identification and Alleviation of False Data Injection Within the Cyber Layer of an Enhanced Distributed Secondary Control in DC Islanded Microgrids

As a result of the integration of communication links within secondary control to circumvent the drawbacks associated with centralized control, microgrids have evolved into cyber-physical systems which are susceptible to cybersecurity risks. This research addresses the repercussions of injecting fake data into the cyber layer within the framework of distributed secondary control in a DC-islanded microgrid. Kullback-Leibler divergence, also referred to as relative entropy, serves as a statistical metric between two probability distributions employed to compute a specific parameter tasked with mitigating the impact on the control system. This parameter comprises the multiplication of two components. The first component is an internal component derived from neighboring entities and provides insight into their security status, indicating whether they have been subjected to attacks. The second component is an external component computed locally and verifies the accuracy of the status received from each neighbor, ensuring its authenticity. Two medium voltage DC microgrids were examined through MATLAB Simulink simulations to corroborate the efficiency of the suggested approach in identifying and mitigating the consequences of false data injection. The results demonstrated that the cybersecurity threat’s influence could be proficiently detected and alleviated in both microgrid configurations, while maintaining the effectiveness of secondary control functions, which are, preserving voltage regulation and precise current distribution. Finally, one of the Microgrids was implemented using OPAL-RT, allowing for the observation of output voltages and currents on a physical hardware oscilloscope.