Adaptive Frequency Regulation Control Method for Wind Farms Based on Deep Reinforcement Learning

TIAN Sheng; LIU Yongqian; TIAN Xinshou; CHI Yongning

doi:10.19805/j.cnki.jcspe.2025.250268

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Adaptive Frequency Regulation Control Method for Wind Farms Based on Deep Reinforcement Learning

更新时间：2026-10-10

- Adaptive Frequency Regulation Control Method for Wind Farms Based on Deep Reinforcement Learning
- Chinese Journal of Power Engineering Vol. 45, Issue 10, Pages: 1689-1695(2025)
- 作者机构：
  
  1. 华北电力大学新能源学院,北京,102206
  2. 华北电力大学国家能源交通融合发展研究院,北京,102206
  3. 中国电力科学研究院,北京,100192
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2025.250268
  CLC：
- Online First：27 May 2025，
  
  Published：2025
- 稿件说明：
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田盛,刘永前,田新首,迟永宁. 基于强化学习的风电场自适应调频控制方法动力工程学报, 2025, 45(10): 1689-1695 https://doi.

org/10.19805/j.cnki.jcspe.2025.250268
田盛,刘永前,田新首,迟永宁. 基于强化学习的风电场自适应调频控制方法动力工程学报, 2025, 45(10): 1689-1695 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.250268.

org/10.19805/j.cnki.jcspe.2025.250268 DOI：

摘要

针对传统自适应调频方法难以根据系统动态变化实时调整控制参数

限制调频性能提升的问题

提出了一种基于双延迟深度确定性策略梯度(twin delayed deep deterministic policy gradient

TD3)深度强化学习算法的风电场自适应调频控制方法。根据电网频率及风电场运行状态

智能体在训练过程中对调频参数进行学习和优化

实现对调频参数的动态自适应调整。结果表明:与传统自适应比例积分(PI)调频控制方法相比

所提方法在不同工况及负载扰动下的最大频率偏差显著降低

频率响应性能得到提升

验证了所提方法的有效性。

Abstract

To address the issue that traditional adaptive frequency regulation methods struggle to adjust control parameters in real time according to dynamic system changes

thereby limiting the enhancement of frequency modulation performance

an adaptive frequency regulation control method for wind farms was proposed

based on the twin delayed DDPG (TD3) deep reinforcement learning algorithm. According to grid frequency and wind farm operating conditions

the intelligent agent optimized and learned the frequency regulation parameters during the training process

achieving dynamic and adaptive adjustment of these parameters. Results show that compared with traditional adaptive proportional-integral (PI) frequency modulation control methods

the proposed approach significantly reduces the maximum frequency deviation under various operating conditions and load disturbances

and improves frequency response performance

validating the effectiveness of the method.

关键词

Keywords

references

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