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Optimization Method of Multi-Mode Model Predictive Control for Wind Farm Reactive Power

Author

Listed:
  • Fei Zhang

    (School of New Energy, North China Electric Power University, Beijing 102206, China
    School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China)

  • Xiaoying Ren

    (School of New Energy, North China Electric Power University, Beijing 102206, China
    School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China)

  • Guidong Yang

    (School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China)

  • Shulong Zhang

    (School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China)

  • Yongqian Liu

    (School of New Energy, North China Electric Power University, Beijing 102206, China)

Abstract

This paper presents a novel approach for optimizing wind farm control through the utilization of a combined model predictive control method. In contrast to conventional methods of controlling active and reactive power in wind farms, the suggested approach integrates a wind power prediction model driven by a neural network and a state-space model for wind turbines. This combination facilitates a more precise forecast of active power, thereby enabling the dynamic prediction of the range of reactive power output from the wind turbines. When combined with the equation of state in wind farm space, it is possible to accurately optimize the reactive power of a wind farm. Furthermore, the impact of active power on voltage fluctuations in the wind farm collector system was examined. The utilization of model predictive control enhances voltage regulation, optimizes system redundancy, and increases the reactive capacity. Sensitivity coefficients were calculated using analytical methods to enhance computational efficiency and to resolve issues related to convergence. In order to validate the proposed methodology and control scheme, a wind farm simulation model comprising 20 turbines was developed to assess the feasibility of the scheme.

Suggested Citation

  • Fei Zhang & Xiaoying Ren & Guidong Yang & Shulong Zhang & Yongqian Liu, 2024. "Optimization Method of Multi-Mode Model Predictive Control for Wind Farm Reactive Power," Energies, MDPI, vol. 17(6), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1287-:d:1353042
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    References listed on IDEAS

    as
    1. Zhang, Fei & Li, Peng-Cheng & Gao, Lu & Liu, Yong-Qian & Ren, Xiao-Ying, 2021. "Application of autoregressive dynamic adaptive (ARDA) model in real-time wind power forecasting," Renewable Energy, Elsevier, vol. 169(C), pages 129-143.
    2. Ren, Xiaoying & Zhang, Fei & Zhu, Honglu & Liu, Yongqian, 2022. "Quad-kernel deep convolutional neural network for intra-hour photovoltaic power forecasting," Applied Energy, Elsevier, vol. 323(C).
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