期刊
IEEE SYSTEMS JOURNAL
卷 16, 期 1, 页码 1487-1498出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSYST.2021.3086145
关键词
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)
This article proposes a method based on MPC and ANNs to detect and mitigate FDIA in dc microgrids. The results show the effectiveness of the proposed strategy in detecting and mitigating attacks.
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.
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