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A novel deep reinforcement learning enabled sparsity promoting adaptive control method to improve the stability of power systems with wind energy penetration

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  • Zhang, Guozhou
  • Hu, Weihao
  • Cao, Di
  • Huang, Qi
  • Chen, Zhe
  • Blaabjerg, Frede

Abstract

With increasing proportion of wind energy in power systems, the intermittence of such energy makes the system run a wide range of operating conditions. In this context, ordinary power system stabilizers (PSS) tuned based on the linearized model of the system at one operating condition may not be able to effectively damp low frequency oscillations (LFO), which brings great challenges to the stability of the system. To this end, this paper proposes a novel sparsity promoting adaptive control method for the online self-tuning of the PSS parameter settings. Different from the existing adaptive control methods, the proposed method combines deep deterministic policy gradient (DDPG) algorithm and sensitivity analysis theory to train an agent to learn the sparse coordinated control policy of multi-PSS. After training, the well-trained agent can be employed for online sparse coordinated adaptive control, and the control signal is only applied, when it is required and only to the key PSS parameters that have the maximum influence on the system stability. Simulation results verify that the proposed method can make the PSS achieve the better performance of damping oscillation and robustness against the change of wind energy in comparison with other methods.

Suggested Citation

  • Zhang, Guozhou & Hu, Weihao & Cao, Di & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2021. "A novel deep reinforcement learning enabled sparsity promoting adaptive control method to improve the stability of power systems with wind energy penetration," Renewable Energy, Elsevier, vol. 178(C), pages 363-376.
    • Handle: RePEc:eee:renene:v:178:y:2021:i:c:p:363-376
    DOI: 10.1016/j.renene.2021.06.081
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    References listed on IDEAS

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    Cited by:

    1. Qingyan Li & Tao Lin & Qianyi Yu & Hui Du & Jun Li & Xiyue Fu, 2023. "Review of Deep Reinforcement Learning and Its Application in Modern Renewable Power System Control," Energies, MDPI, vol. 16(10), pages 1-23, May.
    2. M. A. Hannan & Ali Q. Al-Shetwi & M. S. Mollik & Pin Jern Ker & M. Mannan & M. Mansor & Hussein M. K. Al-Masri & T. M. Indra Mahlia, 2023. "Wind Energy Conversions, Controls, and Applications: A Review for Sustainable Technologies and Directions," Sustainability, MDPI, vol. 15(5), pages 1-30, February.
    3. Zhang, Bin & Hu, Weihao & Xu, Xiao & Li, Tao & Zhang, Zhenyuan & Chen, Zhe, 2022. "Physical-model-free intelligent energy management for a grid-connected hybrid wind-microturbine-PV-EV energy system via deep reinforcement learning approach," Renewable Energy, Elsevier, vol. 200(C), pages 433-448.
    4. Ashok Bhansali & Namala Narasimhulu & Rocío Pérez de Prado & Parameshachari Bidare Divakarachari & Dayanand Lal Narayan, 2023. "A Review on Sustainable Energy Sources Using Machine Learning and Deep Learning Models," Energies, MDPI, vol. 16(17), pages 1-18, August.
    5. Kim, Taewan & Kim, Changwook & Song, Jeonghwan & You, Donghyun, 2024. "Optimal control of a wind farm in time-varying wind using deep reinforcement learning," Energy, Elsevier, vol. 303(C).

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