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Wind turbine power curve modelling under wake conditions using measurements from a spinner-mounted lidar

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  • Sebastiani, Alessandro
  • Angelou, Nikolas
  • Peña, Alfredo

Abstract

Most wind turbines are installed inside wind farms, where they often operate under wake-affected inflow conditions. New methods are required to evaluate the power performance of a wind turbine in wake, as the International Electrotechnical Commission (IEC) standard procedure is applicable only to wake-free turbines. In this work, we investigate the accuracy of a multivariate power curve acquired through a polynomial regression, whose input variables are wind speed and turbulence measurements retrieved upstream of the turbine’s rotor. For this purpose, we use measurements from the SpinnerLidar, a continuous-wave, scanning Doppler lidar measuring the turbine inflow. The SpinnerLidar was mounted in the spinner of a Neg Micon 80 wind turbine located within an onshore wind farm in western Denmark. The input variables are selected among the available lidar measurements with a feature-selection algorithm, resulting in seven input variables, distributed in different locations along the rotor area: six wind speed and one turbulence measurements. The multivariate power curve is tested and compared with IEC-similar power curves under both wake-affected and wake-free conditions. Results show that the multivariate power curve estimates the turbine’s power output more accurately than the IEC-similar power curves, with error reductions up to 66.5% and 34.2% under wake-affected and wake-free conditions, respectively. Furthermore, the multivariate power curve estimates have an accuracy of the same order under both wake-affected and wake-free conditions. Finally, we show that the multivariate model accurately predicts the power even when a simple measuring geometry is used, such as circular scanning pattern with a diameter equal to 90% of the rotor.

Suggested Citation

  • Sebastiani, Alessandro & Angelou, Nikolas & Peña, Alfredo, 2024. "Wind turbine power curve modelling under wake conditions using measurements from a spinner-mounted lidar," Applied Energy, Elsevier, vol. 364(C).
    • Handle: RePEc:eee:appene:v:364:y:2024:i:c:s0306261924003684
    DOI: 10.1016/j.apenergy.2024.122985
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    References listed on IDEAS

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    1. Taslimi-Renani, Ehsan & Modiri-Delshad, Mostafa & Elias, Mohamad Fathi Mohamad & Rahim, Nasrudin Abd., 2016. "Development of an enhanced parametric model for wind turbine power curve," Applied Energy, Elsevier, vol. 177(C), pages 544-552.
    2. Sebastiani, Alessandro & Peña, Alfredo & Troldborg, Niels, 2023. "Numerical evaluation of multivariate power curves for wind turbines in wakes using nacelle lidars," Renewable Energy, Elsevier, vol. 202(C), pages 419-431.
    3. Pelletier, Francis & Masson, Christian & Tahan, Antoine, 2016. "Wind turbine power curve modelling using artificial neural network," Renewable Energy, Elsevier, vol. 89(C), pages 207-214.
    4. Xu, Keyi & Yan, Jie & Zhang, Hao & Zhang, Haoran & Han, Shuang & Liu, Yongqian, 2021. "Quantile based probabilistic wind turbine power curve model," Applied Energy, Elsevier, vol. 296(C).
    5. Marčiukaitis, Mantas & Žutautaitė, Inga & Martišauskas, Linas & Jokšas, Benas & Gecevičius, Giedrius & Sfetsos, Athanasios, 2017. "Non-linear regression model for wind turbine power curve," Renewable Energy, Elsevier, vol. 113(C), pages 732-741.
    6. Manobel, Bartolomé & Sehnke, Frank & Lazzús, Juan A. & Salfate, Ignacio & Felder, Martin & Montecinos, Sonia, 2018. "Wind turbine power curve modeling based on Gaussian Processes and Artificial Neural Networks," Renewable Energy, Elsevier, vol. 125(C), pages 1015-1020.
    7. Saint-Drenan, Yves-Marie & Besseau, Romain & Jansen, Malte & Staffell, Iain & Troccoli, Alberto & Dubus, Laurent & Schmidt, Johannes & Gruber, Katharina & Simões, Sofia G. & Heier, Siegfried, 2020. "A parametric model for wind turbine power curves incorporating environmental conditions," Renewable Energy, Elsevier, vol. 157(C), pages 754-768.
    8. Lydia, M. & Kumar, S. Suresh & Selvakumar, A. Immanuel & Prem Kumar, G. Edwin, 2014. "A comprehensive review on wind turbine power curve modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 452-460.
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