Preprints
https://doi.org/10.5194/wes-2024-28
https://doi.org/10.5194/wes-2024-28
20 Mar 2024
 | 20 Mar 2024
Status: this preprint is currently under review for the journal WES.

On the Uncertainty of Digital Twin Models for Load Monitoring and Fatigue Assessment in Wind Turbine Drivetrains

Felix Christian Mehlan and Amir R. Nejad

Abstract. This article presents a systematic assessment of the uncertainty in digital twins for load and fatigue monitoring in wind turbine drivetrains. The uncertainty in the measurement input, the reduced order drivetrain models and the model updating methods are investigated. A statistical analysis is conducted on gear and bearing load measurements from numerical studies with 5 and 10 MW drivetrain models and from field measurements of a 1.5 MW research turbine. The uncertainty is quantified using log-normal distributions and limitations of digital twin are discussed such as the measurement uncertainty in 10 min averaged SCADA data, the uncertainty in estimating the unknown rotor torque, and the modelling errors in torsional reduced order drivetrain models. This study contributes to a deeper understanding of the origin and the effects of uncertainty in digital twins and delivers a foundation for further reliability and risk assessment studies.

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Felix Christian Mehlan and Amir R. Nejad

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-2024-28', Anonymous Referee #1, 22 Apr 2024
  • RC2: 'Comment on wes-2024-28', Anonymous Referee #2, 25 Apr 2024
  • AC1: 'Comment on wes-2024-28', Felix Christian Mehlan, 23 May 2024
Felix Christian Mehlan and Amir R. Nejad
Felix Christian Mehlan and Amir R. Nejad

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Short summary
A Digital Twin is a virtual representation that mirrors the wind turbine's real behavior through simulation models and sensor measurements and can assist in making key decisions such as planning the replacement of parts. These models and measurements are, of course, not perfect and only give an incomplete picture of the real behavior. This study investigates how large the uncertainty of such models and measurements is and to what extent it affects the decision-making process.
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