Cost-efficient and attack-resilient approaches for state estimation in power grids

State estimation is a fundamental question in a power grid and it is used to understand the state of power grids based on readings of sensors placed at important power grid components. Current state estimation approaches are highly vulnerable to malicious attacks; an attacker can compromise one or a few sensors to mislead state estimation and thus the power grid control algorithms, leading to catastrophic consequences (e.g., a large-scale blackout). This paper presents a series of attack-resilient state estimation algorithms for power grids. These algorithms use the intrinsic relationship among the state variables and the sensor measurements to effectively tolerate malicious sensor readings. This paper also investigates the properties of these algorithms through theoretical analysis and simulation, which both demonstrate the effectiveness of the proposed approaches.

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