| 23 Sep 2026
Brief communication: Enhanced wind turbine fatigue load estimation using digital shadows with data-driven bias correction
Abstract. Building on a previously published digital-shadow framework for wind turbine load estimation, this study augments a linearized aeroelastic model with learning-based bias correction (BC). A neural network (NN) learns operating-condition-dependent corrections while preserving the physics-based model structure. The framework is validated using field measurements under simple, complex, and mixed inflow conditions. For blade bending moments at the 25% span location, damage equivalent load (DEL) errors of 14-24% without correction are reduced to below 5%. The largest improvements occur under complex inflow. A hybrid strategy combines baseline and NN-based corrections, improving robustness across varying operating and inflow conditions.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Wind Energy Science.
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