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

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.