DC microgrids can be considered as cyber-physical systems (CPSs) and they are vulnerable to cyberattacks. Therefore, it is highly recommended to have effective plans to detect and remove cyberattacks in dc microgrids. This article shows how artificial neural networks can help to detect and mitigate coordinated false data injection attacks (FDIAs) on current measurements as a type of cyberattacks in dc microgrids. FDIAs try to inject the false data into the system to disrupt the control application, which can make the dc microgrid shutdown. The proposed method to mitigate FDIAs is a decentralized approach and it has the capability to estimate the value of the false injected data. In addition, the proposed strategy can remove the FDIAs even for unfair attacks with high domains on all units at the same time. The proposed method is tested on a detailed simulated dc microgrid using the MATLAB/Simulink environment. Finally, real-time simulations by OPAL-RT on the simulated dc microgrid are implemented to evaluate the proposed strategy.
|Number of pages||10|
|Journal||IEEE Journal of Emerging and Selected Topics in Power Electronics|
|State||Published - 1 Aug 2021|
Bibliographical noteFunding Information:
Manuscript received April 15, 2020; revised September 27, 2020; accepted October 20, 2020. Date of publication January 11, 2021; date of current version July 30, 2021. The work of Sebastián Rivera was supported in part by the Advanced Center in Electrical and Electronic Engineering (AC3E) Project under Grant ANID/Basal/FB0008 and in part by the Solar Energy Research Center (SERC) Project under Grant ANID/FONDAP/15110019. Recommended for publication by Associate Editor Hao Ma. (Corresponding author: Mohammad Reza Habibi.) Mohammad Reza Habibi, Subham Sahoo, and Frede Blaabjerg are with the Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark (e-mail: email@example.com; firstname.lastname@example.org; email@example.com).
© 2013 IEEE.
- Artificial neural networks
- cyberattack mitigation
- dc microgrid
- false data injection attack (FDIA)