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Maximum wind energy extraction of large-scale wind turbines using nonlinear model predictive control via Yin-Yang grey wolf optimization algorithm

Author

Listed:
  • Song, Dongran
  • Liu, Junbo
  • Yang, Yinggang
  • Yang, Jian
  • Su, Mei
  • Wang, Yun
  • Gui, Ning
  • Yang, Xuebing
  • Huang, Lingxiang
  • Hoon Joo, Young

Abstract

Wind energy extraction for large-scale variable-speed wind turbines could be improved by nonlinear model predictive control. However, the latter entails a sequential global optimization problem with a nonconvex cost function that brings about the heavily computational burden and impedes its real-time application. In this paper, a novel nonlinear model predictive control via Yin-Yang grey wolf optimization algorithm is proposed for maximum wind energy extraction of wind turbines. A dynamic optimization problem with both state and control constraints is constructed, and the single-objective nonlinear function is formulated by using a weighting factor to integrate the extracted wind energy and the generator torque variation within a prediction period of several seconds. On this basis, a complete framework of the nonlinear model predictive control via intelligent algorithm is developed to offer a new paradigm for the design and implementation of the nonlinear model predictive control for wind turbines. Specifically, a new Yin-Yang grey wolf optimization algorithm is proposed, in which the concept of balance between cooperation and competition inspired by Yin-Yang-pair optimization is adopted to achieve the efficient convergence and global optimum. Simulation results verify the superiority of the proposed nonlinear model predictive control via the new Yin-Yang grey wolf optimization algorithm.

Suggested Citation

  • Song, Dongran & Liu, Junbo & Yang, Yinggang & Yang, Jian & Su, Mei & Wang, Yun & Gui, Ning & Yang, Xuebing & Huang, Lingxiang & Hoon Joo, Young, 2021. "Maximum wind energy extraction of large-scale wind turbines using nonlinear model predictive control via Yin-Yang grey wolf optimization algorithm," Energy, Elsevier, vol. 221(C).
    • Handle: RePEc:eee:energy:v:221:y:2021:i:c:s0360544221001158
    DOI: 10.1016/j.energy.2021.119866
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    5. Kashif Sohail & Hooman Farzaneh, 2022. "Model for Optimal Power Coefficient Tracking and Loss Reduction of the Wind Turbine Systems," Energies, MDPI, vol. 15(11), pages 1-19, June.
    6. Zhang, Yagang & Zhao, Yunpeng & Shen, Xiaoyu & Zhang, Jinghui, 2022. "A comprehensive wind speed prediction system based on Monte Carlo and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 305(C).
    7. Hongfu Zhang & Jiahao Wen & Farshad Golnary & Lei Zhou, 2022. "Output Power Control and Load Mitigation of a Horizontal Axis Wind Turbine with a Fully Coupled Aeroelastic Model: Novel Sliding Mode Perspective," Mathematics, MDPI, vol. 10(15), pages 1-40, August.
    8. Tong Shu & Young Hoon Joo, 2023. "Non-Centralised Balance Dispatch Strategy in Waked Wind Farms through a Graph Sparsification Partitioning Approach," Energies, MDPI, vol. 16(20), pages 1-21, October.
    9. Ye, Xuemin & Hu, Jiami & Zheng, Nan & Li, Chunxi, 2023. "Numerical study on aerodynamic performance and noise of wind turbine airfoils with serrated gurney flap," Energy, Elsevier, vol. 262(PB).
    10. Khasanzoda, Nasrullo & Safaraliev, Murodbek & Zicmane, Inga & Beryozkina, Svetlana & Rahimov, Jamshed & Ahyoev, Javod, 2022. "Use of smart grid based wind resources in isolated power systems," Energy, Elsevier, vol. 253(C).
    11. Yashar Mousavi & Geraint Bevan & Ibrahim Beklan Küçükdemiral & Afef Fekih, 2021. "Maximum Power Extraction from Wind Turbines Using a Fault-Tolerant Fractional-Order Nonsingular Terminal Sliding Mode Controller," Energies, MDPI, vol. 14(18), pages 1-16, September.
    12. Song, Dongran & Li, Ziqun & Wang, Lei & Jin, Fangjun & Huang, Chaoneng & Xia, E. & Rizk-Allah, Rizk M. & Yang, Jian & Su, Mei & Joo, Young Hoon, 2022. "Energy capture efficiency enhancement of wind turbines via stochastic model predictive yaw control based on intelligent scenarios generation," Applied Energy, Elsevier, vol. 312(C).
    13. Yanfang Chen & Young-Hoon Joo & Dongran Song, 2021. "Modified Beetle Annealing Search (BAS) Optimization Strategy for Maxing Wind Farm Power through an Adaptive Wake Digraph Clustering Approach," Energies, MDPI, vol. 14(21), pages 1-24, November.
    14. Gao, Xiaoxia & Zhang, Shaohai & Li, Luqing & Xu, Shinai & Chen, Yao & Zhu, Xiaoxun & Sun, Haiying & Wang, Yu & Lu, Hao, 2022. "Quantification of 3D spatiotemporal inhomogeneity for wake characteristics with validations from field measurement and wind tunnel test," Energy, Elsevier, vol. 254(PA).

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