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Wind Energy Science The interactive open-access journal of the European Academy of Wind Energy
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https://doi.org/10.5194/wes-2020-5
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2020-5
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 05 Feb 2020

Submitted as: research article | 05 Feb 2020

Review status
This preprint is currently under review for the journal WES.

Multipoint Reconstruction of Wind Speeds

Christian Behnken, Matthias Wächter, and Joachim Peinke Christian Behnken et al.
  • Institute of Physics/ForWind, University of Oldenburg

Abstract. The most intermittent behavior of atmospheric turbulence is found for very short time scales. Based on a concatenation of conditional probability density functions (cpdfs) of nested wind speeds increments, inspired by a Markov process in scale, we derive a short-time predictor for wind speed fluctuations around a non-stationary mean value and with a corresponding non-stationary variance. As a new quality this short time predictor enables a multipoint reconstruction of wind data. The used cpdfs are (1) directly estimated from historical data from the offshore research platform FINO1 and (2) obtained from numerical solutions of a family of Fokker-Planck equations in the scale domain. The explicit forms of the Fokker-Planck equations are estimated from the given wind data. A good agreement between the statistics of the generated synthetic wind speed fluctuations and the measured is found even on time scales below 1 s. This shows that our approach captures the short-time dynamics of real wind speed fluctuations very well. Our method is extended by taking the non-stationarity of the mean wind speed and its non-stationary variance into account.

Christian Behnken et al.

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Christian Behnken et al.

Christian Behnken et al.

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Latest update: 16 Feb 2020
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Short summary
We extend the common characterisation and modelling of wind time series with respect to higher order statistics. We present an approach which enables to get hold of the general multipoint statistics of wind times series measured. This work is an important step in a more comprehensive description of wind including also extreme events. Important is that we show how stochastic equations can be derived form measured wind data which can be used to model long time series.
We extend the common characterisation and modelling of wind time series with respect to higher...
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