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

被引：73

作者：

Habibi, Mohammad Reza ^{[1
]}

Baghaee, Hamid Reza ^{[2
]}

Dragicevic, Tomislav ^{[3
]}

Blaabjerg, Frede ^{[1
]}

机构：

[1] Aalborg Univ, Dept Energy Technol, DK-9220 Aalborg, Denmark

[2] Amirkabir Univ Technol, Dept Elect Engn, Tehran 158754413, Iran

[3] Tech Univ Denmark, Dept Elect Engn, DK-2800 Lyngby, Denmark

来源：

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | 2021年 / 68卷 / 02期

关键词：

Microgrids; Biological neural networks; Neurons; Voltage control; Training; Circuits and systems; Artificial neural networks; cyber-attack; DC microgrid; droop control; false data injection attack;

D O I：

10.1109/TCSII.2020.3011324

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：717 / 721

页数：5