False Data Injection Cyber-Attacks Mitigation in Parallel DC/DC Converters Based on Artificial Neural Networks
Mohammad Reza Habibi, Hamid Reza Baghaee, Tomislav Dragičević, Frede Blaabjerg
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.
Topics & Concepts
MicrogridVoltage droopConvertersArtificial neural networkComputer scienceVoltageControl (management)EngineeringElectronic engineeringControl theory (sociology)Electrical engineeringArtificial intelligenceVoltage regulatorSmart Grid Security and ResilienceSecurity and Verification in ComputingNetwork Security and Intrusion Detection