Fast Frequency Control Strategy Based on Worst-Case Network Attack Perception

Abstract

A fast frequency control (FFC) strategy using the proximal policy optimization based on worst-case network attack perception (worst-case PPO) algorithm is proposed to address the complexity of fast frequency control in power systems and the risks posed by network attacks. This strategy focuses on frequency stability in power systems with a high penetration of renewable energy, and utilizes a reinforcement learning agent to intelligently adjust the power setpoint of voltage source converters (VSCs), ensuring that both the frequency and the rate of change of the frequency remain within permissible limits. Considering the potential for network attacks, this strategy adopts the robust worst-case PPO algorithm, which ensures system stability even under the worst-case attack scenarios. The experimental results demonstrate that the proposed strategy effectively prevents frequency degradation under various disturbances, exhibiting a stronger disturbance resistance and robustness compared to traditional reinforcement learning methods. Furthermore, the strategy is easy to implement, highly adaptable, and suitable for the complex and dynamic operational environment of power systems, providing strong support for the secure and stable operation of smart grids.

Keywords

• voltage source converters
• frequency control
• worst-case PPO algorithm
• worst attacks
• smart grids