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Simulating the wake flow effect of wind turbines on velocity and turbulence using particle random walk method

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  • Song, MengXuan
  • Wu, BingHeng
  • Chen, Kai
  • Zhang, Xing
  • Wang, Jun

Abstract

This paper presents a novel way of simulating the effect of velocity decay and turbulence of wind turbine's wake flow. By decoupling the solving of wake flow from that of the velocity field, the proposed model treats the wake flow intensity as a kind of convective and diffusive virtual matter. The particle random walk method is utilized to simulate the motion of the virtual matter. Comparing to the existing linear model for turbine wake flow, the proposed model can predict the distributions of velocity decay and turbulence of wake flow in a non-uniform flow field above complex terrain. Experimental data from wind tunnel and real wind farm is used to validate the model, demonstrating its effectiveness on estimating the velocity decay and the turbulence intensity, and additionally, the power yield of a wind farm. The model proposed in this paper can be integrated into algorithms for numerical assessment and micro-siting optimization of wind farms on complex terrains.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:116:y:2016:i:p1:p:583-591
    DOI: 10.1016/j.energy.2016.09.107
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    References listed on IDEAS

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    1. Croonenbroeck, Carsten & Dahl, Christian Møller, 2014. "Accurate medium-term wind power forecasting in a censored classification framework," Energy, Elsevier, vol. 73(C), pages 221-232.
    2. González, Javier Serrano & Gonzalez Rodriguez, Angel G. & Mora, José Castro & Santos, Jesús Riquelme & Payan, Manuel Burgos, 2010. "Optimization of wind farm turbines layout using an evolutive algorithm," Renewable Energy, Elsevier, vol. 35(8), pages 1671-1681.
    3. Marmidis, Grigorios & Lazarou, Stavros & Pyrgioti, Eleftheria, 2008. "Optimal placement of wind turbines in a wind park using Monte Carlo simulation," Renewable Energy, Elsevier, vol. 33(7), pages 1455-1460.
    4. 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.
    5. 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.
    6. Song, M.X. & Chen, K. & Zhang, X. & Wang, J., 2015. "The lazy greedy algorithm for power optimization of wind turbine positioning on complex terrain," Energy, Elsevier, vol. 80(C), pages 567-574.
    7. 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.
    8. Croonenbroeck, Carsten & Møller Dahl, Christian, 2014. "Accurate medium-term wind power forecasting in a censored classification framework," Discussion Papers 351, European University Viadrina Frankfurt (Oder), Department of Business Administration and Economics.
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    Cited by:

    1. Fei, Zhao & Tengyuan, Wang & Xiaoxia, Gao & Haiying, Sun & Hongxing, Yang & Zhonghe, Han & Yu, Wang & Xiaoxun, Zhu, 2020. "Experimental study on wake interactions and performance of the turbines with different rotor-diameters in adjacent area of large-scale wind farm," Energy, Elsevier, vol. 199(C).
    2. Sun, Haiying & Yang, Hongxing & Gao, Xiaoxia, 2023. "Investigation into wind turbine wake effect on complex terrain," Energy, Elsevier, vol. 269(C).
    3. Sun, Haiying & Yang, Hongxing, 2020. "Numerical investigation of the average wind speed of a single wind turbine and development of a novel three-dimensional multiple wind turbine wake model," Renewable Energy, Elsevier, vol. 147(P1), pages 192-203.

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