18 Dec 2023
 | 18 Dec 2023
Status: this preprint is currently under review for the journal WES.

Data-driven surrogate model for wind turbine damage equivalent load

Rad Haghi and Curran Crawford

Abstract. Aeroelastic simulations are used to assess wind turbines in accordance with IEC standards in the time domain. Doing so can calculate fatigue and extreme loads on the wind turbine's components. These simulations are conducted for several reasons, such as reducing safety margins in wind turbine component design by covering a wide range of uncertainties in wind and wave conditions, and meeting the requirements of the digital twin, which needs a thorough set of simulations for calibration. Thus, it's essential to develop computationally efficient yet accurate models that can replace costly aeroelastic simulations and data processing. We suggest a data-driven approach to build surrogate models for the Damage Equivalent Load (DEL) based on aeroelastic simulation outputs to tackle this challenge. Our method provides a quick and efficient way to calculate DEL using wind input signals without the need of time-consuming aeroelastic simulations. Our study will focus on utilizing a sequential machine-learning method to map wind speed time series to DEL. Furthermore, we demonstrate the versatility of the developed and trained surrogate models by testing them for a wind turbine in the wake and using transfer learning to enhance their prediction.

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Rad Haghi and Curran Crawford

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-2023-157', Francisco de Nolasco Santos, 12 Jan 2024
  • RC2: 'Comment on wes-2023-157', Anonymous Referee #2, 17 Jan 2024
Rad Haghi and Curran Crawford
Rad Haghi and Curran Crawford


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Short summary
The journal paper focuses on developing surrogate models for predicting the Damage Equivalent Load (DEL) on wind turbines without needing extensive aeroelastic simulations. The study emphasizes the development of a sequential machine-learning architecture for this purpose. The study also explores implementing simplified wake models and transfer learning to enhance the models' prediction capabilities in various wind conditions.