Virtual Sensing of Wind Turbine Hub Loads and Drivetrain Fatigue Damage

Felix Mehlan, Jonathan Keller, Amir Nejad

Research output: Contribution to journalArticlepeer-review

1 Scopus Citations

Abstract

This paper presents a Digital Twin for virtual sensing of wind turbine aerodynamic hub loads, as well as monitoring the accumulated fatigue damage and remaining useful life in drivetrain bearings based on measurements of the Supervisory Control and Data Acquisition (SCADA) and the drivetrain condition monitoring system (CMS). The aerodynamic load estimation is realized with data-driven regression models, while the estimation of local bearing loads and damage is conducted with physics-based, analytical models. Field measurements of the DOE 1.5 research turbine are used for model training and validation. The results show low errors of 6.4% and 1.1% in the predicted damage at the main and the generator side high-speed bearing respectively.

Original languageAmerican English
Pages (from-to)207-218
Number of pages12
JournalForschung im Ingenieurwesen/Engineering Research
Volume87
Issue number1
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s).

NREL Publication Number

  • NREL/JA-5000-84880

Keywords

  • digital twin
  • remaining useful life
  • virtual sensing

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