False Data Injection Attacks and the Distributed Countermeasure in DC Microgrids
Mengxiang Liu, Chengcheng Zhao, Ruilong Deng, Peng Cheng, Jiming Chen
Abstract
In this article, we consider a hierarchical control-based dc microgrid (DCmG) equipped with unknown input observer (UIO)-based detectors, where the potential false data injection (FDI) attacks and the distributed countermeasure are investigated. First, we find that the vulnerability of the UIO-based detector originates from the lack of knowledge of true inputs. Zero trace stealthy (ZTS) attacks can be launched by secretly faking the unknown inputs, under which the detection residual will not be altered, and the impact on the DCmG in terms of voltage balancing and current sharing is theoretically analyzed. Then, to mitigate the ZTS attack, we propose an automatic and timely countermeasure based on the average point of common coupling voltage obtained from the dynamic average consensus (DAC) estimator. The integrity of the communicated data utilized in DAC estimators is guaranteed via UIO-based detectors, where the DAC parameters are perturbed in a fixed period to be concealed from attackers. Finally, the detection and mitigation performance of the proposed countermeasure is rigorously investigated, and extensive simulations are conducted in Simulink/PLECS to validate the theoretical results.