DEEP REINFORCEMENT LEARNING OPTIMIZATION OF NONLINEAR ACTIVE DISTURBANCE REJECTION CONTROL FOR MICROGRID VOLTAGE STABILIZATION

Zhou Xuesong; Liu Yaorong; Ma Youjie; Tao Long; Wang Xinyue; Wen Hulong

doi:10.19912/j.0254-0096.tynxb.2025-0003

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (6) : 1-9. DOI: 10.19912/j.0254-0096.tynxb.2025-0003

DEEP REINFORCEMENT LEARNING OPTIMIZATION OF NONLINEAR ACTIVE DISTURBANCE REJECTION CONTROL FOR MICROGRID VOLTAGE STABILIZATION

Zhou Xuesong¹, Liu Yaorong¹, Ma Youjie¹, Tao Long¹, Wang Xinyue¹, Wen Hulong^2,3

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Abstract

To address the problem of poor voltage stability of wind-solar-energy storage DC microgrids in distributed renewable energy systems, an optimized nonlinear active disturbance rejection control strategy based on the SAC algorithm （SAC-ADRC） is proposed. Firstly, the wind-solar-energy storage system is modeled with nonlinear ADRC control. Then, the gain parameters of the nonlinear ADRC are reconstructed by using linear/nonlinear ADRC switching to improve its internal parameters which are more difficult to tune and analyze. Finally, the analysis establishes a mechanism for SAC intelligence to learn interactively with the microgrid environment, enabling the adjustment of non-linear ADRC parameters. Comparative analysis using algorithm convergence curves and simulation of various classical working conditions confirms the superiority of the SAC-ADRC control strategy in terms of interference performance. Thus, it is shown that the organic integration of nonlinear ADRC and deep reinforcement learning improves the stability of the microgrid bus voltage.

Key words

microgrid / deep reinforcement learning / active disturbance rejection control / disturbance rejection / parameter setting

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Zhou Xuesong, Liu Yaorong, Ma Youjie, Tao Long, Wang Xinyue, Wen Hulong. DEEP REINFORCEMENT LEARNING OPTIMIZATION OF NONLINEAR ACTIVE DISTURBANCE REJECTION CONTROL FOR MICROGRID VOLTAGE STABILIZATION[J]. Acta Energiae Solaris Sinica. 2026, 47(6): 1-9 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0003

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