Optimal False Data Injection Attack against Automatic Generation Control in Power Grids

This paper studies false data injection attacks against automatic generation control (AGC), a fundamental control system used in all power grids to maintain the grid frequency at a nominal value. Attacks on the sensor measurements for AGC can cause frequency excursion that triggers remedial actions such as disconnecting customer loads or generators, leading to blackouts and potentially costly equipment damage. We derive an attack impact model and analyze an optimal attack, consisting of a series of false data injections, that minimizes the remaining time until the onset of remedial actions, leaving the shortest time for the grid to counteract. We show that, based on eavesdropped sensor data and a few feasible-to-obtain system constants, the attacker can learn the attack impact model and achieve the optimal attack in practice. This paper provides essential understanding on the limits of physical impact of false data injections on power grids, and provides an analysis framework to guide the protection of sensor data links. Our analysis and algorithms are validated by experiments on a physical 16-bus power system testbed and extensive simulations based on a 37-bus power system model.