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Wake flow model of wind turbine using particle simulation

Author

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  • Song, M.X.
  • Chen, K.
  • He, Z.Y.
  • Zhang, X.

Abstract

A particle model for calculating turbine wake flow during the optimization of wind farm micro-siting is presented. This model treats the wake flow as virtual particles generated by the turbine rotor. Based on the pre-calculated flow field of empty wind farm, the motions of the particles are simulated. The wake flow effect of velocity decrement is obtained from the density of the particles. On flat terrain, the proposed particle model fits the experimental data better than the previous linear model. On complex terrain, since the particle simulation is based on numerical result of flow field, the particle model can be coupled with optimization algorithm of generating turbine layout.

Suggested Citation

  • Song, M.X. & Chen, K. & He, Z.Y. & Zhang, X., 2012. "Wake flow model of wind turbine using particle simulation," Renewable Energy, Elsevier, vol. 41(C), pages 185-190.
    • Handle: RePEc:eee:renene:v:41:y:2012:i:c:p:185-190
    DOI: 10.1016/j.renene.2011.10.016
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    References listed on IDEAS

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    1. Grady, S.A. & Hussaini, M.Y. & Abdullah, M.M., 2005. "Placement of wind turbines using genetic algorithms," Renewable Energy, Elsevier, vol. 30(2), pages 259-270.
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    Cited by:

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    3. Zhang, Yagang & Yang, Jingyun & Wang, Kangcheng & Wang, Zengping & Wang, Yinding, 2015. "Improved wind prediction based on the Lorenz system," Renewable Energy, Elsevier, vol. 81(C), pages 219-226.
    4. Song, MengXuan & Wu, BingHeng & Chen, Kai & Zhang, Xing & Wang, Jun, 2016. "Simulating the wake flow effect of wind turbines on velocity and turbulence using particle random walk method," Energy, Elsevier, vol. 116(P1), pages 583-591.
    5. Tao, Siyu & Xu, Qingshan & Feijóo, Andrés & Zheng, Gang & Zhou, Jiemin, 2020. "Nonuniform wind farm layout optimization: A state-of-the-art review," Energy, Elsevier, vol. 209(C).
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    9. Song, M.X. & Chen, K. & He, Z.Y. & Zhang, X., 2014. "Optimization of wind farm micro-siting for complex terrain using greedy algorithm," Energy, Elsevier, vol. 67(C), pages 454-459.
    10. Reddy, Sohail R., 2021. "A machine learning approach for modeling irregular regions with multiple owners in wind farm layout design," Energy, Elsevier, vol. 220(C).
    11. Song, Mengxuan & Chen, Kai & Zhang, Xing & Wang, Jun, 2016. "Optimization of wind turbine micro-siting for reducing the sensitivity of power generation to wind direction," Renewable Energy, Elsevier, vol. 85(C), pages 57-65.
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    15. Al-Shammari, Eiman Tamah & Shamshirband, Shahaboddin & Petković, Dalibor & Zalnezhad, Erfan & Yee, Por Lip & Taher, Ros Suraya & Ćojbašić, Žarko, 2016. "Comparative study of clustering methods for wake effect analysis in wind farm," Energy, Elsevier, vol. 95(C), pages 573-579.
    16. Wang, Longyan & Tan, Andy C.C. & Gu, Yuantong & Yuan, Jianping, 2015. "A new constraint handling method for wind farm layout optimization with lands owned by different owners," Renewable Energy, Elsevier, vol. 83(C), pages 151-161.
    17. Nikolić, Vlastimir & Shamshirband, Shahaboddin & Petković, Dalibor & Mohammadi, Kasra & Ćojbašić, Žarko & Altameem, Torki A. & Gani, Abdullah, 2015. "Wind wake influence estimation on energy production of wind farm by adaptive neuro-fuzzy methodology," Energy, Elsevier, vol. 80(C), pages 361-372.
    18. Chen, K. & Song, M.X. & Zhang, X. & Wang, S.F., 2016. "Wind turbine layout optimization with multiple hub height wind turbines using greedy algorithm," Renewable Energy, Elsevier, vol. 96(PA), pages 676-686.
    19. Song, M.X. & Chen, K. & He, Z.Y. & Zhang, X., 2013. "Bionic optimization for micro-siting of wind farm on complex terrain," Renewable Energy, Elsevier, vol. 50(C), pages 551-557.
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