False Data Injection Cyber-Attacks Mitigation in Parallel DC/DC Converters Based on Artificial Neural Networks

Mohammad Reza Habibi, Hamid Reza Baghaee, Tomislav Dragicevic, Frede Blaabjerg

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Because of the existence of communication networks and control applications, DC microgrids can be attacked by cyber-attackers. False data injection attack (FDIA) is one type of cyber-attacks where attackers try to inject false data to the target DC microgrid to destruct the control system. This brief discusses the effect of FDIAs in DC microgrids that are structured by parallel DC/DC converters and they are controlled by droop based control strategies to maintain the desired DC voltage level. Also, an effective and proper strategy based on an artificial neural network-based reference tracking application is introduced to remove the FDIAs in the DC microgrid.
Original languageEnglish
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Volume68
Issue number2
Pages (from-to)717 - 721
ISSN1549-7747
DOIs
Publication statusPublished - 2021

Keywords

  • Artificial neural networks
  • Cyber-attack
  • DC microgrid
  • Droop control
  • False data injection attack.

Fingerprint Dive into the research topics of 'False Data Injection Cyber-Attacks Mitigation in Parallel DC/DC Converters Based on Artificial Neural Networks'. Together they form a unique fingerprint.

Cite this