Detection of False Data Injection Cyber-Attacks in DC Microgrids Based on Recurrent Neural Networks

Cyber-physical systems (CPSs) are vulnerable to cyber-attacks. Nowadays, the detection of cyber-attacks in microgrids as examples of CPS has become an important topic due to their wide use in various practical applications, from renewable energy plants to power distribution and electric transportation. In this paper, we propose a new artificial intelligence (AI)-based method for detection of cyber-attacks in direct current (DC) microgrids and also identification of the attacked distributed energy resource (DER) unit. The proposed method works based on time series analysis and a nonlinear auto-regressive exogenous model (NARX) neural network, which is a special type of recurrent neural network, for estimating DC voltages and currents. In the proposed method, we consider the effect of cyber-attacks named false data injection attacks (FDIAs), which try to affect the accurate voltage regulation and current sharing by affecting voltage and current sensors. In the presented strategy, first, a DC microgrid is operated and controlled without any FDIAs to gather enough data during the normal operation required for the training of NARX neural networks. It is worth mentioning that, in the data generation process, load changing is also considered to have distinguishing datasets for load changing and cyber-attack scenarios. Trained and fine-tuned NARX neural networks are exploited in an online manner to estimate the output DC voltages and currents of DER units in DC microgrid. Then, based on the error of estimation, the cyber-attack is detected. To show the effectiveness of the proposed method, offline digital time-domain simulation studies are performed on a test DC microgrid system in MATLAB/Simulink environment, and the results are verified using real-time simulations using OPAL-RT real-time digital simulator (RTDS).