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A parametric model for wind turbine power curves incorporating environmental conditions

Author

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  • Saint-Drenan, Yves-Marie
  • Besseau, Romain
  • Jansen, Malte
  • Staffell, Iain
  • Troccoli, Alberto
  • Dubus, Laurent
  • Schmidt, Johannes
  • Gruber, Katharina
  • Simões, Sofia G.
  • Heier, Siegfried

Abstract

A wind turbine’s power curve relates its power production to the wind speed it experiences. The typical shape of a power curve is well known and has been studied extensively. However, power curves of individual turbine models can vary widely from one another. This is due to both the technical features of the turbine (power density, cut-in and cut-out speeds, limits on rotational speed and aerodynamic efficiency), and environmental factors (turbulence intensity, air density, wind shear and wind veer). Data on individual power curves are often proprietary and only available through commercial databases. We therefore develop an open-source model for pitch regulated horizontal axis wind turbine which can generate the power curve of any turbine, adapted to the specific conditions of any site. This can employ one of six parametric models advanced in the literature, and accounts for the eleven variables mentioned above. The model is described, the impact of each technical and environmental feature is examined, and it is then validated against the manufacturer power curves of 91 turbine models. Versions of the model are made available in MATLAB, R and Python code for the community.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:157:y:2020:i:c:p:754-768
    DOI: 10.1016/j.renene.2020.04.123
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    References listed on IDEAS

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    3. Yongnian Zhao & Yu Xue & Shanhong Gao & Jundong Wang & Qingcai Cao & Tao Sun & Yan Liu, 2022. "Computation and Analysis of an Offshore Wind Power Forecast: Towards a Better Assessment of Offshore Wind Power Plant Aerodynamics," Energies, MDPI, vol. 15(12), pages 1-17, June.
    4. 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.
    5. McKenna, Russell & Pfenninger, Stefan & Heinrichs, Heidi & Schmidt, Johannes & Staffell, Iain & Bauer, Christian & Gruber, Katharina & Hahmann, Andrea N. & Jansen, Malte & Klingler, Michael & Landwehr, 2022. "High-resolution large-scale onshore wind energy assessments: A review of potential definitions, methodologies and future research needs," Renewable Energy, Elsevier, vol. 182(C), pages 659-684.
    6. Tuy, Soklin & Lee, Han Soo & Chreng, Karodine, 2022. "Integrated assessment of offshore wind power potential using Weather Research and Forecast (WRF) downscaling with Sentinel-1 satellite imagery, optimal sites, annual energy production and equivalent C," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    7. Yang, Jian & Wang, Li & Song, Dongran & Huang, Chaoneng & Huang, Liansheng & Wang, Junlei, 2022. "Incorporating environmental impacts into zero-point shifting diagnosis of wind turbines yaw angle," Energy, Elsevier, vol. 238(PA).
    8. Mehrjoo, Mehrdad & Jafari Jozani, Mohammad & Pawlak, Miroslaw, 2021. "Toward hybrid approaches for wind turbine power curve modeling with balanced loss functions and local weighting schemes," Energy, Elsevier, vol. 218(C).
    9. Yang, Mao & Shi, Chaoyu & Liu, Huiyu, 2021. "Day-ahead wind power forecasting based on the clustering of equivalent power curves," Energy, Elsevier, vol. 218(C).
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    11. Davide Astolfi & Francesco Castellani & Andrea Lombardi & Ludovico Terzi, 2021. "Multivariate SCADA Data Analysis Methods for Real-World Wind Turbine Power Curve Monitoring," Energies, MDPI, vol. 14(4), pages 1-18, February.
    12. Usama Aziz & Sylvie Charbonnier & Christophe Berenguer & Alexis Lebranchu & Frederic Prevost, 2022. "A Multi-Turbine Approach for Improving Performance of Wind Turbine Power-Based Fault Detection Methods," Energies, MDPI, vol. 15(8), pages 1-21, April.
    13. Justyna Zalewska & Krzysztof Damaziak & Jerzy Malachowski, 2021. "An Energy Efficiency Estimation Procedure for Small Wind Turbines at Chosen Locations in Poland," Energies, MDPI, vol. 14(12), pages 1-18, June.
    14. Francisco Bilendo & Angela Meyer & Hamed Badihi & Ningyun Lu & Philippe Cambron & Bin Jiang, 2022. "Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review," Energies, MDPI, vol. 16(1), pages 1-38, December.
    15. Wang, Peng & Li, Yanting & Zhang, Guangyao, 2023. "Probabilistic power curve estimation based on meteorological factors and density LSTM," Energy, Elsevier, vol. 269(C).

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