Paper Info
Title
Secure MPC/ANN-Based False Data Injection Cyber-Attack Detection and Mitigation in DC Microgrids
Abstract
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.
Year
DOI
Venue
2022
10.1109/JSYST.2021.3086145
IEEE SYSTEMS JOURNAL
Keywords
DocType
Volume
Microgrids, Biological neural networks, Communication networks, Predictive models, Observers, Neurons, Cost function, Artificial neural network (ANN), cyber-physical dc microgrid, false-data injection attack (FDIA), model predictive control (MPC)
Journal
16
Issue
ISSN
Citations 
1
1932-8184
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Mohammad Reza Habibi131.07
Hamid Reza Baghaee200.68
Frede Blaabjerg31195248.32
Tomislav Dragicevic400.34