AUTHOR=Lin Xixiang , An Dou , Cui Feifei , Zhang Feiye TITLE=False data injection attack in smart grid: Attack model and reinforcement learning-based detection method JOURNAL=Frontiers in Energy Research VOLUME=Volume 10 - 2022 YEAR=2023 URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.1104989 DOI=10.3389/fenrg.2022.1104989 ISSN=2296-598X ABSTRACT=The smart grid, as a cyber-physical system, is vulnerable to attacks due to the diversified and open environment. The false data injection attack (FDIA) can threaten the grid security by constructing and injecting the falsified attack vector to bypass the system detection. Due to the diversity of attacks, it’s impractical to detect FDIAs by fixed methods. This paper proposed a FDIA model and countering detection methods based on deep reinforcement learning (DRL). First, we studied an attack model under the assumption of unlimited attack resources and information of complete topology. Different types of FDIAs are also enumerated. Then, we formulated the attack detection problem as a Markov decision process (MDP). A deep reinforcement learning-based method is proposed to detect FDIAs. To address the sparse reward problem, experiences with discrepant rewards are stored in different replay buffers to achieve efficiency. Moreover, the state space is extended by considering the most recent states to improve the perception capability. Simulations were performed on IEEE 9,14,30, and 57-bus systems, proving the validation of attack model and efficiency of detection method. Results proved efficacy of the detection method in different scenarios.