Antijamming Game Framework for Secure State Estimation in Power Systems

作者：He, Jiasheng; Chen, Cailian^*; Zhu, Shanying; Yang, Bo; Guan, Xinping

来源：IEEE Transactions on Industrial Informatics, 2019, 15(5): 2628-2637.

DOI：10.1109/TII.2018.2871933

• Jamming attack
• multiagent reinforcement learning
• state estimation
• stochastic game

In this paper, we investigate the secure state estimation (SSE) problem in power systems, where the physical system is measured by meters and mainly focuses on the measurements sent to a remote estimator via wireless networks faced with jamming attacks. Malicious attacks on the transmission paths block the data transmission and deteriorate the performance of estimation. Various works have been proposed to cope with the transmission failure. Few of them have considered the case that a smart attacker could adjust strategies according to the defensive methods with advanced communication techniques. We propose the antijamming game framework for SSE. Under this framework, defensive path selection (DPS) is proposed based onmultiagent reinforcement learning to make optimal path selection against the intelligent attacker and improve the transmission performance for SSE. The effectiveness of the proposed method is theoretically proved to improve the robustness of estimation and the capability of DPS is analyzed.

• 出版日期2019-5
• 单位上海交通大学