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Validation of kinematic wind turbine wake models in complex terrain using actual windfarm production data

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  • Seim, Fredrik
  • Gravdahl, Arne R.
  • Adaramola, Muyiwa S.

Abstract

Measurements from a wind farm in northern Norway have been used in an attempt to validate three kinematic wake models, which are often preferred due to their efficiency in terms of calculation time. Assisted by the commercial CFD-based WindSim software, the accuracy of the Jensen-, Larsen- and Ishihara model are tested in eight single-wake cases with regard to several key aspects. Due to the complex terrain at the site, a range of issues complicated the validation procedure. The Larsen model correlated well with the measured data regarding the normalized power deficit, while both the Jensen- and Ishihara model clearly overestimated the power deficit. At the wake centerline, the Larsen model was by far the most accurate, with a mean absolute error of 7%. The Jensen- and Ishihara model had a mean absolute error of 21% and 34% respectively. Both the Jensen- and Ishihara model agreed well with the observed wake width. The Larsen model widely overestimated the wake width in all cases, but with an almost constant offset. For the energy loss in the wake, the Larsen model performed best for the three investigated wake cases with a mean absolute error of 29%, although all the three wake models showed a varying performance with a tendency to underestimate the energy loss.

Suggested Citation

  • Seim, Fredrik & Gravdahl, Arne R. & Adaramola, Muyiwa S., 2017. "Validation of kinematic wind turbine wake models in complex terrain using actual windfarm production data," Energy, Elsevier, vol. 123(C), pages 742-753.
    • Handle: RePEc:eee:energy:v:123:y:2017:i:c:p:742-753
    DOI: 10.1016/j.energy.2017.01.140
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    References listed on IDEAS

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    1. Adaramola, M.S. & Krogstad, P.-Å., 2011. "Experimental investigation of wake effects on wind turbine performance," Renewable Energy, Elsevier, vol. 36(8), pages 2078-2086.
    2. Adaramola, Muyiwa S., 2012. "Estimating global solar radiation using common meteorological data in Akure, Nigeria," Renewable Energy, Elsevier, vol. 47(C), pages 38-44.
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    2. Zheng, Yue & Wang, Jie-Sheng & Zhu, Jun-Hua & Zhang, Xin-Yue & Xing, Yu-Xuan & Zhang, Yun-Hao, 2024. "MORSA: Multi-objective reptile search algorithm based on elite non-dominated sorting and grid indexing mechanism for wind farm layout optimization problem," Energy, Elsevier, vol. 293(C).
    3. Pacheco de Sá Sarmiento, Franciene Izis & Goes Oliveira, Jorge Luiz & Passos, Júlio César, 2022. "Impact of atmospheric stability, wake effect and topography on power production at complex-terrain wind farm," Energy, Elsevier, vol. 239(PC).
    4. Daniel Tabas & Jiannong Fang & Fernando Porté-Agel, 2019. "Wind Energy Prediction in Highly Complex Terrain by Computational Fluid Dynamics," Energies, MDPI, vol. 12(7), pages 1-12, April.
    5. Sun, Haiying & Gao, Xiaoxia & Yang, Hongxing, 2020. "A review of full-scale wind-field measurements of the wind-turbine wake effect and a measurement of the wake-interaction effect," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    6. Sun, Haiying & Yang, Hongxing & Gao, Xiaoxia, 2023. "Investigation into wind turbine wake effect on complex terrain," Energy, Elsevier, vol. 269(C).
    7. Zahid Mehmood & Zhenyu Wang & Xin Zhang & Guiying Shen, 2024. "Aerodynamic Performance and Numerical Validation Study of a Scaled-Down and Full-Scale Wind Turbine Models," Energies, MDPI, vol. 17(21), pages 1-22, October.
    8. Antonini, Enrico G.A. & Romero, David A. & Amon, Cristina H., 2020. "Optimal design of wind farms in complex terrains using computational fluid dynamics and adjoint methods," Applied Energy, Elsevier, vol. 261(C).
    9. Yuan Li & Zengjin Xu & Zuoxia Xing & Bowen Zhou & Haoqian Cui & Bowen Liu & Bo Hu, 2020. "A Modified Reynolds-Averaged Navier–Stokes-Based Wind Turbine Wake Model Considering Correction Modules," Energies, MDPI, vol. 13(17), pages 1-19, August.
    10. Liang, Xiaoling & Fu, Shifeng & Cai, Fulin & Han, Xingxing & Zhu, Weijun & Yang, Hua & Shen, Wenzhong, 2023. "Experimental investigation on wake characteristics of wind turbine and a new two-dimensional wake model," Renewable Energy, Elsevier, vol. 203(C), pages 373-381.
    11. Famoso, Fabio & Brusca, Sebastian & D'Urso, Diego & Galvagno, Antonio & Chiacchio, Ferdinando, 2020. "A novel hybrid model for the estimation of energy conversion in a wind farm combining wake effects and stochastic dependability," Applied Energy, Elsevier, vol. 280(C).
    12. Syed Ahmed Kabir, Ijaz Fazil & Safiyullah, Ferozkhan & Ng, E.Y.K. & Tam, Vivian W.Y., 2020. "New analytical wake models based on artificial intelligence and rivalling the benchmark full-rotor CFD predictions under both uniform and ABL inflows," Energy, Elsevier, vol. 193(C).

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