Secure MPC/ANN-Based False Data Injection Cyber-Attack Detection and Mitigation in DC Microgrids

Direct current (DC) microgrids can be considered as cyber-physical systems due to implementation of measurement devices, communication network, and control layers. Consequently, dc are also vulnerable cyber-attacks. False-data injection attacks (FDIAs) a common type cyber-attacks, which try inject false data into the system in order cause defective behavior. This article proposes method based on model predictive (MPC) artificial neural networks (ANNs) detect mitigate FDIA that formed by parallel dc–dc converters. The proposed MPC/ANN-based strategy shows how MPC ANNs coordinated provide secure layer remove FDIAs microgrid. In strategy, an ANN plays role estimator implement cyber-attack detection mitigation strategy. is examined under different conditions, physical events cyber disturbances (i.e. load changing delay, time-varying attack), results MPC-based scheme compared with conventional proportional-integral controllers. obtained show effectiveness attack microgrids.