FAST FREQUENCY RESPONSE OPTIMIZATION STRATEGY FOR WIND TURBINES BASED ON DEEP REINFORCEMENT LEARNING

Li Chuanliang; Liu Jian; Qian Minhui; Yang Dejian; Chu Xiaowei

doi:10.19912/j.0254-0096.tynxb.2025-0615

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (8) : 781-789. DOI: 10.19912/j.0254-0096.tynxb.2025-0615

FAST FREQUENCY RESPONSE OPTIMIZATION STRATEGY FOR WIND TURBINES BASED ON DEEP REINFORCEMENT LEARNING

Li Chuanliang¹, Liu Jian¹, Qian Minhui², Yang Dejian¹, Chu Xiaowei¹

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Abstract

With the increasing penetration of wind power, the decrease in system inertia raises concerns about frequency stability. To fully utilize the frequency regulation potential of renewable energy, this paper proposes an optimization strategy for rapid frequency response of wind turbines based on deep reinforcement learning. Firstly, a dynamic frequency response model of wind turbines, considering the frequency regulation dead zone, is constructed using time-domain analysis. The model reveals the correlation between multiple parameters, such as the dead zone threshold, primary frequency regulation gain, and wind power penetration, and grid frequency deviation, through decoupling analysis. By this method, establish a multi-dimensional correlation quantitative characterization of frequency dynamic characteristics under the combined action of multiple parameters. Secondly, to address the challenge of identifying the optimal solution set in traditional frequency regulation methods, a dynamic parameter optimization framework based on DDPG is designed, enabling adaptive parameter adjustment through agent-environment interaction. Finally, simulations of a power system with wind turbines connected to the grid verify that the proposed strategy performs better in frequency support and adaptability than traditional parameter settings and particle swarm algorithms under different wind speeds and disturbance scenarios.

Key words

wind turbines / wind turbine frequency regulation / fast frequency support / multi-dimensional parameter coupling / deep reinforcement learning / parameter optimization

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Li Chuanliang, Liu Jian, Qian Minhui, Yang Dejian, Chu Xiaowei. FAST FREQUENCY RESPONSE OPTIMIZATION STRATEGY FOR WIND TURBINES BASED ON DEEP REINFORCEMENT LEARNING[J]. Acta Energiae Solaris Sinica. 2026, 47(8): 781-789 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0615

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