TY - GEN
T1 - Deep reinforcement learning for der cyber-attack mitigation
AU - Roberts, Ciaran
AU - Ngo, Sy Toan
AU - Milesi, Alexandre
AU - Peisert, Sean
AU - Arnold, Daniel
AU - Saha, Shammya
AU - Scaglione, Anna
AU - Johnson, Nathan
AU - Kocheturov, Anton
AU - Fradkin, Dmitriy
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/11
Y1 - 2020/11/11
N2 - The increasing penetration of DER with smart-inverter functionality is set to transform the electrical distribution network from a passive system, with fixed injection/consumption, to an active network with hundreds of distributed controllers dynamically modulating their operating setpoints as a function of system conditions. This transition is being achieved through standardization of functionality through grid codes and/or international standards. DER, however, are unique in that they are typically neither owned nor operated by distribution utilities and, therefore, represent a new emerging attack vector for cyber-physical attacks. Within this work we consider deep reinforcement learning as a tool to learn the optimal parameters for the control logic of a set of uncompromised DER units to actively mitigate the effects of a cyber-attack on a subset of network DER.
AB - The increasing penetration of DER with smart-inverter functionality is set to transform the electrical distribution network from a passive system, with fixed injection/consumption, to an active network with hundreds of distributed controllers dynamically modulating their operating setpoints as a function of system conditions. This transition is being achieved through standardization of functionality through grid codes and/or international standards. DER, however, are unique in that they are typically neither owned nor operated by distribution utilities and, therefore, represent a new emerging attack vector for cyber-physical attacks. Within this work we consider deep reinforcement learning as a tool to learn the optimal parameters for the control logic of a set of uncompromised DER units to actively mitigate the effects of a cyber-attack on a subset of network DER.
UR - http://www.scopus.com/inward/record.url?scp=85099471933&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099471933&partnerID=8YFLogxK
U2 - 10.1109/SmartGridComm47815.2020.9302997
DO - 10.1109/SmartGridComm47815.2020.9302997
M3 - Conference contribution
AN - SCOPUS:85099471933
T3 - 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020
BT - 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020
Y2 - 11 November 2020 through 13 November 2020
ER -