False Data Injection Cyber-Attacks Detection for Multiple DC Microgrid Clusters. (15th March 2022) - Tan, Sen; Xie, Peilin; Guerrero, Josep M.; Vasquez, Juan C.
Abstract: DC microgrids are considered as the next generation of power systems because of the possibility of connecting various renewable energy sources to different types of loads based on distributed networks. However, due to the strong reliance on communication networks, DC microgrids are vulnerable to intentional cyber-attacks. Therefore, in this paper, a robust cyber-attack detection scheme is proposed for DC microgrid systems. Utilizing the parity-based method, a multi-objective optimization problem is formulated to achieve robust detection against electrical parameter perturbations and unknown disturbances. An analytical solution is then provided using the singular value decomposition approach. With the disturbance decoupling scheme, the presented detection strategy can monitor the system with only local knowledge of the DC microgrid. The proposed method is easy to design and with less computation complexity. The performances of the provided scheme are validated by simulation tests and experimental results. Highlights: A comprehensive attack detection scheme for distributed DC microgrids. Cyber-attack detection techniques that rely on parity-based relations. Robust detection against microgrid modeling uncertainties. Improved sensitivity to the residuals by optimization method.