Indemnity for Frequency Disruption in a Smart Grid during Cyber–Attack

2017 ◽  
Author(s):  
M. A. Rahman ◽  
M. S. Rana ◽  
M. S. Anower
Keyword(s):  
Smart Grid ◽  
Cyber Attack

scholarly journals Mitigating the Risk of Cyber Attack on Smart Grid Systems

2014 ◽  
Vol 28 ◽  
pp. 575-582 ◽  
Author(s):  
Eric B. Rice ◽  
Anas AlMajali
Keyword(s):  
Smart Grid ◽  
Cyber Attack ◽  
Grid Systems

scholarly journals Cognitive Risk Control for Mitigating Cyber-Attack in Smart Grid

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 125806-125826 ◽  
Author(s):  
Mohammad Irshaad Oozeer ◽  
Simon Haykin
Keyword(s):  
Smart Grid ◽  
Risk Control ◽  
Cyber Attack ◽  
Cognitive Risk

Cyber attack impact on critical Smart Grid infrastructures

ISGT 2014 ◽  
2014 ◽  
Author(s):  
Kallisthenis I. Sgouras ◽  
Athina D. Birda ◽  
Dimitris P. Labridis
Keyword(s):  
Smart Grid ◽  
Cyber Attack ◽  
Grid Infrastructures

scholarly journals Cognitive Dynamic System for AC State Estimation and Cyber-Attack Detection in Smart Grid

2020 ◽  
Author(s):  
Mohammad Irshaad Oozeer ◽  
Simon Haykin
Keyword(s):  
Smart Grid ◽  
Dynamic System ◽  
State Estimation ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Data Detection ◽  
Cyber Attack ◽  
Bad Data Detection ◽  
Cycle Basis ◽  
Iterative Procedures

The work presented in this chapter is an extension of our previous research of bringing together the Cognitive Dynamic System (CDS) and the Smart Grid (SG) by focusing on AC state estimation and Cyber-Attack detection. Under the AC power flow model, state estimation is complex and computationally expensive as it relies on iterative procedures. On the other hand, the False Data Injection (FDI) attacks are a new category of cyber-attacks targeting the SG that can bypass the current bad data detection techniques in the SG. Due to the complexity of the nonlinear system involved, the amount of published works on AC based FDI attacks have been fewer compared to their DC counterpart. Here, we will demonstrate how the entropic state, which is the objective function of the CDS, can be used as a metric to monitor the grid’s health and detect FDI attacks. The CDS, acting as the supervisor of the system, improves the entropic state on a cycle to cycle basis by dynamically optimizing the state estimation process through the reconfiguration of the weights of the sensors in the network. In order to showcase performance of this new structure, computer simulations are carried out on the IEEE 14-bus system for optimal state estimation and FDI attack detection.

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