Power Grid AC-based State Estimation: Vulnerability Analysis Against Cyber Attacks

To ensure grid efficiency and reliability, powersystem operators continuously monitor the operational char-acteristics of the grid through a critical process called stateestimation (SE), which performs the task by filtering and fusingvarious measurements collected from grid sensors. This studyanalyzes the vulnerability of the key operation module, namelyAC-based SE, against potential cyber attacks on data integrity,also known as false data injection attack (FDIA). A generalform of FDIA can be formulated as an optimization problem,whose objective is to find a stealthy and sparse data injectionvector on the sensor measurements with the aim of making thestate estimate spurious and misleading. Due to the nonlinearAC measurement model and the cardinality constraint, theproblem includes both continuous and discrete nonlinearities.To solve the FDIA problem efficiently, we propose a novelconvexification framework based on semidefinite programming(SDP). By analyzing a globally optimal SDP solution, we delineatethe “attackable region” for any given set of measurement typesand grid topology, where the spurious state can be falsified byFDIA. Furthermore, we prove that the attack is stealthy andsparse, and derive performance bounds. Simulation results onvarious IEEE test cases indicate the efficacy of the proposedconvexification approach. From the grid protection point of view,the results of this study can be used to design a security metricfor the current practice against cyber attacks, redesign the baddata detection scheme, and inform proposals of grid hardening.From a theoretical point of view, the proposed framework canbe used for other nonconvex problems in power systems andbeyond.