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Finding the Worse Case: Undetectable False Data Injection with Minimized Knowledge and Resource

Accurate state estimation is crucial to smart grid operations. Following the identification of false data injection attacks (FDIA), numerous research has been proposed, yet most of them assume the worst-case scenario where attackers face few constraints on the full knowledge of the system topology or on the attack resource they can leverage to compromise the meters. In this work, we formulate attackerâ€™s knowledge and resource as two critical constraints and propose an FDIA model that generates the attack vector with no prior knowledge of the grid topology and minimal access to the measurements. The work adopts the existing solution based on principal component analysis (PCA) to generate the stealth attack vector and leverages particle swarm optimization (PSO) to directly minimize the l0-norm of the attack vector. Considering the feasibility of practical attacks, our work also enforces constraints on the convergence of state estimation and the significance of induced error, so that the generated attack vector is guaranteed undetectable yet impactful. Simulation results on the IEEE 30- bus system have demonstrated the minimized sparsity with topology-blindness, attack- stealthiness, and significant impact on the state variables of the proposed FDIA scheme, which will help refine the risk evaluation and inform better mitigation efforts against such threats.