Preprints
https://doi.org/10.5194/wes-2023-45
https://doi.org/10.5194/wes-2023-45
09 May 2023
 | 09 May 2023
Status: a revised version of this preprint is currently under review for the journal WES.

Wind speed time series synthesis using a parametrized power spectral density function

Ram C. Poudel, David Corbus, and Ian Baring-Gould

Abstract. We propose a new method to synthesize 1 Hz wind speed and wind power time series data from the industry standard 10-minute wind turbine performance data. The method is based on a parameterized power spectral density (PSD) function decomposed into trend and random components. We illustrate the intra-timestep data synthesis utilizing 1 Hz data from two distributed wind turbines: CART3 (600 kW) and NPS (100 kW).

Ram C. Poudel et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2023-45', Anonymous Referee #1, 24 May 2023
    • AC1: 'Reply on RC1', Ram Poudel, 31 May 2023
  • RC2: 'Comment on wes-2023-45', Anonymous Referee #2, 08 Aug 2023
    • AC2: 'Reply on RC2', Ram Poudel, 12 Aug 2023

Ram C. Poudel et al.

Ram C. Poudel et al.

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Short summary
We propose a new method to synthesize 1 Hz wind speed and wind power time series data from the industry standard 10-minute wind turbine performance data. The method is based on a parameterized power spectral density (PSD) function decomposed into trend and random components. We illustrate the intra-timestep data synthesis utilizing 1 Hz data from two distributed wind turbines.