TY - JOUR
T1 - Detection of Malicious Attacks in Autonomous Cyber-Physical Inverter-Based Microgrids
AU - Zografopoulos, Ioannis
AU - Konstantinou, Charalambos
N1 - KAUST Repository Item: Exported on 2021-12-14
PY - 2021
Y1 - 2021
N2 - The distributed generation capabilities of microgrids (MGs) arise as essential assets in enhancing grid resilience. The integration of distributed energy sources (DERs), controllable loads, and prosumers necessitates the deployment of potent control and communication synergies. While those synergies transform MGs into cyber-physical systems through information technologies able to sense, control, and actuate local resources and loads, they inadvertently expose MGs to cyber-attack threats. Increasing the security of critical communication and control systems against ‘black swan’ events, i.e., high-impact low-probability cyber-physical incidents, is a major priority for MGs operations. Such incidents, if left unabated, can intensify and elicit system dynamics instabilities, eventually causing outages and system failures. In this paper, we develop an integrated approach for multi-agent MG systems able to perform detection of malicious cyber-physical attacks based on subspace methods. We employ the small signal model of an autonomous/islanded MG and consider different attack models targeting the MG's secondary frequency control. The attack detector is constructed via identifying the stable kernel representation of the autonomous cyber-physical MG in the attack-free case. We illustrate the impact of the attack models as well as the feasibility of the developed detection method in simulation models of the Canadian urban benchmark distribution system.
AB - The distributed generation capabilities of microgrids (MGs) arise as essential assets in enhancing grid resilience. The integration of distributed energy sources (DERs), controllable loads, and prosumers necessitates the deployment of potent control and communication synergies. While those synergies transform MGs into cyber-physical systems through information technologies able to sense, control, and actuate local resources and loads, they inadvertently expose MGs to cyber-attack threats. Increasing the security of critical communication and control systems against ‘black swan’ events, i.e., high-impact low-probability cyber-physical incidents, is a major priority for MGs operations. Such incidents, if left unabated, can intensify and elicit system dynamics instabilities, eventually causing outages and system failures. In this paper, we develop an integrated approach for multi-agent MG systems able to perform detection of malicious cyber-physical attacks based on subspace methods. We employ the small signal model of an autonomous/islanded MG and consider different attack models targeting the MG's secondary frequency control. The attack detector is constructed via identifying the stable kernel representation of the autonomous cyber-physical MG in the attack-free case. We illustrate the impact of the attack models as well as the feasibility of the developed detection method in simulation models of the Canadian urban benchmark distribution system.
UR - http://hdl.handle.net/10754/673926
UR - https://ieeexplore.ieee.org/document/9633019/
U2 - 10.1109/TII.2021.3132131
DO - 10.1109/TII.2021.3132131
M3 - Article
SN - 1941-0050
SP - 1
EP - 1
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
ER -