REINFORCEMENT LEARNING ACTIVE DISTURBANCE REJECTION CONTROL STRATEGY FOR MICROGRID ENERGY STORAGE SIDE DC-DC CONVERTER

Ma Youjie; Liu Yiming; Zhou Xuesong; Wang Bo; Tao Long; Wen Hulong

doi:10.19912/j.0254-0096.tynxb.2023-1955

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REINFORCEMENT LEARNING ACTIVE DISTURBANCE REJECTION CONTROL STRATEGY FOR MICROGRID ENERGY STORAGE SIDE DC-DC CONVERTER

更新时间：2025-10-18

- REINFORCEMENT LEARNING ACTIVE DISTURBANCE REJECTION CONTROL STRATEGY FOR MICROGRID ENERGY STORAGE SIDE DC-DC CONVERTER
- Vol. 46, Issue 3, Pages: 63-72(2025)
- 作者机构：
  
  1. 天津理工大学天津市新能源电力变换传输与智能控制重点实验室,天津 300384
  2. 天津瑞能电气有限公司,天津 300381
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2023-1955
  CLC： TM615
- Published Online：07 April 2025，
  
  Published：2025
- 稿件说明：
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马幼捷, 刘熠铭, 周雪松, 王博, 陶珑, 问虎龙. 微网储能侧DC-DC变换器的强化学习自抗扰控制策略[J]. 太阳能学报, 2025, 46(3): 63-72.

Ma Youjie, Liu Yiming, Zhou Xuesong, et al. REINFORCEMENT LEARNING ACTIVE DISTURBANCE REJECTION CONTROL STRATEGY FOR MICROGRID ENERGY STORAGE SIDE DC-DC CONVERTER[J]. 2025, 46(3): 63-72.
马幼捷, 刘熠铭, 周雪松, 王博, 陶珑, 问虎龙. 微网储能侧DC-DC变换器的强化学习自抗扰控制策略[J]. 太阳能学报, 2025, 46(3): 63-72. DOI： 10.19912/j.0254-0096.tynxb.2023-1955.

Ma Youjie, Liu Yiming, Zhou Xuesong, et al. REINFORCEMENT LEARNING ACTIVE DISTURBANCE REJECTION CONTROL STRATEGY FOR MICROGRID ENERGY STORAGE SIDE DC-DC CONVERTER[J]. 2025, 46(3): 63-72. DOI： 10.19912/j.0254-0096.tynxb.2023-1955.

摘要

直流微电网电压稳定性研究是新型电力系统面临的关键问题。针对微电网系统中直流母线电压波动大和抗干扰能力弱等缺陷

该文提出一种由 Q-learning算法赋能的DC-DC变换器自抗扰控制策略。通过引入线性扩张状态观测器

实现对模型内部摄动与外部扰动量的精确估计与补偿

利用Q-learning 算法实现控制策略参数自适应优化

从而更高效地维持输出电压稳定。基于理论分析

推导范数意义下的Q-learning算法收敛性

并运用Lyapunov理论判据证明线性自抗扰的稳定性。最后

通过仿真对比该文提出的控制策略、线性自抗扰控制与双闭环PI控制在不同工况下的结果

充分验证该策略在提升 DC-DC 变换器抗扰能力和鲁棒水平的高效性与优越性。

Abstract

The study of voltage stability in DC microgrids is a key issue faced by new power systems. This paper proposes a active disturbance rejection control strategy for DC-DC converters empowered by Q-learning algorithm to address the shortcomings of large DC bus voltage fluctuations and weak anti-interference ability in microgrid systems. By introducing a linear expansion state observer

precise estimation and compensation of internal and external disturbances in the model are achieved. Q-learning algorithm is used to achieve adaptive optimization of control strategy parameters

thereby maintaining output voltage stability more efficiently. Based on theoretical analysis

the convergence of Q-learning algorithm in the norm sense was derived

and the stability of linear active disturbance rejection was proved using Lyapunov theory criterion. Finally

by comparing the results of the proposed control strategy

linear active disturbance rejection control

and dual closed-loop PI control under different operating conditions through simulation

the efficiency and superiority of this strategy in improving the disturbance rejection ability and robustness level of DC-DC converters are fully verified.

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Keywords

references

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