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Most of the traditional state estimation algorithms are provided false alarm when there is attack. This paper proposes an attack-resilient algorithm where attack is automatically ignored, and the state estimation process is continuing which acts a grid-eye for monitoring whole power systems. After modeling the smart grid incorporating distributed energy resources, the smart sensors are deployed to gather measurement information where sensors are prone to attacks. Based on the noisy and cyber attack measurement information, the optimal state estimation algorithm is designed. When the attack is happened, the measurement residual error dynamic goes high and it can ignore using proposed saturation function. Moreover, the proposed saturation function is automatically computed in a dynamic way considering residual error and deigned parameters. Combing the aforementioned approaches, the Kalman filter algorithm is modified which is applied to the smart grid state estimation. The simulation results show that the proposed algorithm provides high estimation accuracy.
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Resilient Distributed Filter for State Estimation of Cyber-Physical Systems Under Attack
Proliferation of distributed Cyber-Physical Systems has raised the need for developing computationally efficient security solutions. Toward this objective, distributed state estimators that can withstand attacks on agents (or nodes) of the system have been developed, but many of these works consider the estimation error to asymptotically converge to zero by restricting the number of agents that can be compromised. We propose Resilient Distributed Kalman Filter (RDKF), a novel distributed algorithm that estimates states within an error bound and does not depend on the number of agents that can be compromised by an attack. Our method is based on convex optimization and performs well in practice, which we demonstrate with the help of a simulation example. We theoretically show that, in a connected network, the estimation error generated by the Distributed Kalman Filter and our RDKF at each agent converges to zero in an attack free and noise free scenario. Furthermore, our resiliency analysis result shows that the RDKF algorithm bounds the disturbance on the state estimate caused by an attack.
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- Award ID(s):
- 1818500
- PAR ID:
- 10105497
- Date Published:
- Journal Name:
- American Control Conference
- Page Range / eLocation ID:
- 5141-5147
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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