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The operation of power grids is becoming increasingly data-centric. While theabundance of data could improve the efficiency of the system, it poses majorreliability challenges. In particular, state estimation aims to learn thebehavior of the network from data but an undetected attack on this problemcould lead to a large-scale blackout. Nevertheless, understanding vulnerabilityof state estimation against cyber attacks has been hindered by the lack oftools studying the topological and data-analytic aspects of the network.Algorithmic robustness is of critical need to extract reliable information fromabundant but untrusted grid data. We propose a robust state estimationframework that leverages network sparsity and data abundance. For a large-scalepower grid, we quantify, analyze, and visualize the regions of the networkprone to cyber attacks. We also propose an optimization-based graphicalboundary defense mechanism to identify the border of the geographical areawhose data has been manipulated. The proposed method does not allow a localattack to have a global effect on the data analysis of the entire network,which enhances the situational awareness of the grid especially in the face ofadversity. The developed mathematical framework reveals key geometric andalgebraic factors that can affect algorithmic robustness and is used to studythe vulnerability of the U.S. power grid in this paper.

Boundary Defense against Cyber Threat for Power System Operation