A parametric model for wind turbine power curves incorporating environmental conditions
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Abstract (arXiv)
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, the 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 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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Sep 2019.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
3 papers.
- 2022Physically Meaningful Uncertainty Quantification in Probabilistic Wind Turbine Power Curve Models as a Damage Sensitive Featurecited by 4
- 2021Probabilistic Neural Network to Quantify Uncertainty of Wind Power Estimationcited by 2
- 2026Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensemblescited by 0
- 2026Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensembles13 Feb 2026
- 2022Physically Meaningful Uncertainty Quantification in Probabilistic Wind Turbine Power Curve Models as a Damage Sensitive Feature30 Sep 2022
- 2021Probabilistic Neural Network to Quantify Uncertainty of Wind Power Estimation4 Jun 2021