Articles | Volume 31, issue 4
https://doi.org/10.5194/npg-31-587-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-31-587-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Dec 2024
Research article |
| 10 Dec 2024
| 10 Dec 2024
Multifractal analysis of wind turbine power and rainfall from an operational wind farm – Part 1: Wind turbine power and the associated biases
HM&Co, École nationale des ponts et chaussées, Institut Polytechnique de Paris, Champs-sur-Marne, France
Auguste Gires
HM&Co, École nationale des ponts et chaussées, Institut Polytechnique de Paris, Champs-sur-Marne, France
Yelva Roustan
CEREA, École nationale des ponts et chaussées, Institut Polytechnique de Paris, EDF R&D, Île-de-France, France
Ernani Schnorenberger
Boralex, Lyon, France
Ioulia Tchiguirinskaia
HM&Co, École nationale des ponts et chaussées, Institut Polytechnique de Paris, Champs-sur-Marne, France
Daniel Schertzer
HM&Co, École nationale des ponts et chaussées, Institut Polytechnique de Paris, Champs-sur-Marne, France
Department of Civil and Environmental Engineering, Imperial College London, London, UK
Department of Complexity Science, Potsdam Institute for Climate Impact Research, Potsdam, Germany
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To understand the influence of rainfall on wind power production, turbine power and rainfall were measured simultaneously on an operational wind farm and analysed. The correlation between wind, wind power, air density, and other fields was obtained on various temporal scales under rainy and dry conditions. An increase in the correlation was observed with an increase in the rain; rain also influenced the correspondence between actual and expected values of power at various velocities.
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Hai Zhou, Daniel Schertzer, and Ioulia Tchiguirinskaia
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Yangzi Qiu, Igor da Silva Rocha Paz, Feihu Chen, Pierre-Antoine Versini, Daniel Schertzer, and Ioulia Tchiguirinskaia
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Our original research objective is to investigate the uncertainties of the hydrological responses of nature-based solutions (NBSs) that result from the multiscale space variability in both the rainfall and the NBS distribution. Results show that the intersection effects of spatial variability in rainfall and the spatial arrangement of NBS can generate uncertainties of peak flow and total runoff volume estimations in NBS scenarios.
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Short summary
Wind energy exhibits extreme variability in space and time. However, it also shows scaling properties (properties that remain similar across different times and spaces of measurement). This can be quantified using appropriate statistical tools. In this way, the scaling properties of power from a wind farm are analysed here. Since every turbine is manufactured by design for a rated power, this acts as an upper limit on the data. This bias is identified here using data and numerical simulations.
Wind energy exhibits extreme variability in space and time. However, it also shows scaling...
