Reinforcement Learning Based Vulnerability Analysis for Smart Grids Against False Data Injection Attacks

Abstract

False data injection attacks modify the meter measurements to mislead the control center into estimating inaccurate system states and thus affect the reliable operation of smart grids. In this paper, we propose a deep reinforcement learning based vulnerability analysis scheme for smart grids that enables the control center to construct an attack vector from the attacker’s view to identify the vulnerable meters. The control center chooses the attack vector based on power system states, meter measurements, the previous number of analyzed meters, and injected errors without knowing the power system topology. This scheme designs an actor-critic architecture that applies an actor network to output the policy probability distribution to handle the continuous and high-dimensional vulnerability analysis policy and contains a critic network to guide the weights update of the actor network. We also analyze the computational complexity and perform simulations to verify the efficacy of this scheme in terms of the vulnerability detection rate, the number of analyzed meters and the utility.