On the modeling errors of digital twins for load monitoring and fatigue assessment in wind turbine drivetrains

Abstract

This article presents a systematic assessment of the modeling and estimation errors of digital twins for load and fatigue monitoring in wind turbine drivetrains. The errors 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 error distributions are quantified using normal distributions, and limitations of the digital twin are discussed such as the information loss of 10 min averaged supervisory control and data acquisition system (SCADA) data, the estimation errors of the unknown rotor torque, and the modeling 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.