Brief communication: Enhanced wind turbine fatigue load estimation using digital shadows with data-driven bias correction

Abstract. This study proposes a digital shadow framework for wind turbine load estimation that integrates a linearized industrial-grade aeroelastic model with a deep learning–based bias correction (BC) method. To address model mismatches and limited inflow representation, a learning-based bias correction strategy is introduced, where static bias terms are first calibrated via wind-speed-dependent fitting, followed by perturbed correction profiles and parametric simulations to construct a digital shadow dataset. A neural network (NN) is then trained to map operating conditions and bias parameters to load estimation errors, enabling adaptive correction under unseen conditions.

The proposed method is validated using field data spanning diverse inflow conditions, achieving a reduction in blade bending moment DEL prediction errors at the 25 % span location from 15–25 % to below 5 %. This demonstrates strong robustness and improved capture of inflow–structure interactions. Overall, the framework provides a scalable pathway to data-driven digital shadows and a foundation for future digital twin applications in real-time load estimation and operational optimization.