TY - GEN
T1 - Secure Control of DC Microgrids under Cyber-Attacks based on Recurrent Neural Networks
AU - Habibi, Mohammad Reza
AU - Dragicevic, Tomislav
AU - Blaabjerg, Frede
PY - 2020
Y1 - 2020
N2 - DC microgrids have advantages when comparing to AC microgrids, i.e., more efficiency and less complexity to control. To control the DC microgrids, communication networks, and measurement devices such as voltage and current sensors are needed to be implemented to measure desired data and transmit them. Because of the existence of the communication network, the DC microgrids can be vulnerable to cyber-attacks. False data injection attacks (FDIAs) are a type of cyber-attacks that try to inject false data into the system. In DC microgrids, FDIAs can destruct the control application of the system by destroying the control objectives, i.e., current sharing and voltage regulation. This work introduces a method based on recurrent neural networks to detect and mitigate FDIAs in DC microgrids. The proposed strategy is based on a reference tracking application and it can also estimate the value of the false injected data. It is important to note that the proposed strategy is a decentralized approach and it needs only the local data and it does not need the information from other units. The performance of the proposed method is examined under different conditions. The obtained results prove the effectiveness of the proposed cyber-attack detection and mitigation strategy.
AB - DC microgrids have advantages when comparing to AC microgrids, i.e., more efficiency and less complexity to control. To control the DC microgrids, communication networks, and measurement devices such as voltage and current sensors are needed to be implemented to measure desired data and transmit them. Because of the existence of the communication network, the DC microgrids can be vulnerable to cyber-attacks. False data injection attacks (FDIAs) are a type of cyber-attacks that try to inject false data into the system. In DC microgrids, FDIAs can destruct the control application of the system by destroying the control objectives, i.e., current sharing and voltage regulation. This work introduces a method based on recurrent neural networks to detect and mitigate FDIAs in DC microgrids. The proposed strategy is based on a reference tracking application and it can also estimate the value of the false injected data. It is important to note that the proposed strategy is a decentralized approach and it needs only the local data and it does not need the information from other units. The performance of the proposed method is examined under different conditions. The obtained results prove the effectiveness of the proposed cyber-attack detection and mitigation strategy.
KW - DC microgrid
KW - Recurrent neural networks
KW - Secure control
KW - False data injection attack
KW - Current sharing
U2 - 10.1109/PEDG48541.2020.9244459
DO - 10.1109/PEDG48541.2020.9244459
M3 - Article in proceedings
SN - 9781728169903
T3 - 2020 Ieee 11th International Symposium on Power Electronics for Distributed Generation Systems (pedg)
SP - 517
EP - 521
BT - Proceedings of 2020 IEEE 11th International Symposium on Power Electronics for Distributed Generation Systems
PB - IEEE
T2 - IEEE 11<sup>th</sup> International Symposium on Power Electronics for Distributed Generation Systems
Y2 - 28 September 2020 through 1 October 2020
ER -