Year: 2024
Type: article
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.
Source: Mathematics
DOI: 10.3390/math13010132
Authors: Wentao Xu, Zhenghang Song, Peiyuan Guan
Institutions: Northeastern University, Zhejiang University, University of Oslo
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,955
OpenAlex ID: W4405940632