Boundary Defense Against Cyber Threat for Power System State Estimation

The operation of power grids is becoming increasingly data-centric. While the abundance of data could improve system efficiency, it poses major reliability challenges. In particular, state estimation aims to find the operating state of a network from the telemetered data, but an undetected attack on the data could lead to making wrong operational decisions for the system and trigger a large-scale blackout. Nevertheless, understanding the vulnerability of state estimation with regards to cyberattacks, which is a special instance of graph-structured quadratic sensing problem, has been hindered by the lack of tools for studying the topological and data-analytic aspects of networks. Algorithmic robustness is critical in extracting reliable information from abundant but untrusted grid data. For a large-scale power grid, we quantify, analyze, and visualize the regions of the network that are not robust to cyberattacks in the sense that there exists a data manipulation strategy for each of those local regions that misleads the operator at the global scale and yields a wrong estimation of the state of the network at almost all buses. We also propose an optimization-based graphical boundary defense mechanism to identify the border of the geographical area in which data have been manipulated. The proposed method does not allow a local attack 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 of adversity. The developed mathematical framework reveals key geometric and algebraic factors that can affect algorithmic robustness and is used to study the vulnerability of the U.S. power grid in this paper.