A Multistage Game in Smart Grid Security: A Reinforcement Learning Solution

• Published in: IEEE transaction on neural networks and learning systems Vol. 30; no. 9; pp. 2684 - 2695
• Main Authors: Ni, Zhen, Paul, Shuva
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Electric power; Electric power grids; Electricity distribution; Game theory; Games; Learning; Machine learning; Markov decision process and vulnerability analysis; Multistage; multistage game; Power system dynamics; Power transmission lines; Reinforcement; Reinforcement learning; Security; Smart grid; smart grid security; Smart grids; Transmission lines
• DOI: 10.1109/TNNLS.2018.2885530
• ISSN: 2162-237X; 2162-2388

Existing smart grid security research investigates different attack techniques and cascading failures from the attackers' viewpoints, while the defenders' or the operators' protection strategies are somehow neglected. Game theoretic methods are applied for the attacker-defender games in the smart grid security area. Yet, most of the existing works only use the one-shot game and do not consider the dynamic process of the electric power grid. In this paper, we propose a new solution for a multistage game (also called a dynamic game) between the attacker and the defender based on reinforcement learning to identify the optimal attack sequences given certain objectives (e.g., transmission line outages or generation loss). Different from a one-shot game, the attacker here learns a sequence of attack actions applying for the transmission lines and the defender protects a set of selected lines. After each time step, the cascading failure will be measured, and the line outage (and/or generation loss) will be used as the feedback for the attacker to generate the next action. The performance is evaluated on W&W 6-bus and IEEE 39-bus systems. A comparison between a multistage attack and a one-shot attack is conducted to show the significance of the multistage attack. Furthermore, different protection strategies are evaluated in simulation, which shows that the proposed reinforcement learning solution can identify optimal attack sequences under several attack objectives. It also indicates that attacker's learned information helps the defender to enhance the security of the system.