TY - JOUR
T1 - Secure MPC/ANN-Based False Data Injection Cyber-Attack Detection and Mitigation in DC Microgrids
AU - Habibi, Mohammad Reza
AU - Reza Baghaee, Hamid
AU - Blaabjerg, Frede
AU - Dragičević, Tomislav
PY - 2022/3
Y1 - 2022/3
N2 - Direct current (DC) microgrids can be considered as cyber-physical systems due to implementation of measurement devices, communication network, and control layers. Consequently, dc microgrids are also vulnerable to cyber-attacks. False-data injection attacks (FDIAs) are a common type of cyber-attacks, which try to inject false data into the system in order to cause the defective behavior. This article proposes a method based on model predictive control (MPC) and artificial neural networks (ANNs) to detect and mitigate the FDIA in dc microgrids that are formed by parallel dc–dc converters. The proposed MPC/ANN-based strategy shows how MPC and ANNs can be coordinated to provide a secure control layer to detect and remove the FDIAs in the dc microgrid. In the proposed strategy, an ANN plays the role of the estimator to implement in the cyber-attack detection and mitigation strategy. The proposed method is examined under different conditions, physical events and cyber disturbances (i.e. load changing and communication delay, and time-varying attack), and the results of the MPC-based scheme is compared with conventional proportional-integral controllers. The obtained results show the effectiveness of the proposed strategy to detect and mitigate the attack in dc microgrids.
AB - Direct current (DC) microgrids can be considered as cyber-physical systems due to implementation of measurement devices, communication network, and control layers. Consequently, dc microgrids are also vulnerable to cyber-attacks. False-data injection attacks (FDIAs) are a common type of cyber-attacks, which try to inject false data into the system in order to cause the defective behavior. This article proposes a method based on model predictive control (MPC) and artificial neural networks (ANNs) to detect and mitigate the FDIA in dc microgrids that are formed by parallel dc–dc converters. The proposed MPC/ANN-based strategy shows how MPC and ANNs can be coordinated to provide a secure control layer to detect and remove the FDIAs in the dc microgrid. In the proposed strategy, an ANN plays the role of the estimator to implement in the cyber-attack detection and mitigation strategy. The proposed method is examined under different conditions, physical events and cyber disturbances (i.e. load changing and communication delay, and time-varying attack), and the results of the MPC-based scheme is compared with conventional proportional-integral controllers. The obtained results show the effectiveness of the proposed strategy to detect and mitigate the attack in dc microgrids.
KW - Artificial neural network (ANN)
KW - cyber-physical dc microgrid
KW - false-data injection attack (FDIA)
KW - model predictive control (MPC)
UR - http://www.scopus.com/inward/record.url?scp=85112220906&partnerID=8YFLogxK
U2 - 10.1109/JSYST.2021.3086145
DO - 10.1109/JSYST.2021.3086145
M3 - Journal article
SN - 1932-8184
VL - 16
SP - 1487
EP - 1498
JO - I E E E Systems Journal
JF - I E E E Systems Journal
IS - 1
M1 - 9470914
ER -