A Learning-Based Algorithm for Modeling Blade Degradation in Wind Turbines
by Decoupling Lift and Drag Coefficient for a Physics-Informed Approach
M.A. Heine (TU Delft - Electrical Engineering, Mathematics and Computer Science)
H. Schuttelaars – Mentor (TU Delft - Mathematical Physics)
S. P. Mulders – Mentor (TU Delft - Team Mulders)
Donatella Zappalà – Mentor (TU Delft - Wind Energy)
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Abstract
As the world shifts toward sustainable energy, offshore wind power is becoming increasingly important because of its ability to capture stronger and more consistent winds. However, offshore wind turbines operate in harsh environmental conditions, exposing wind turbine blades to loads. These conditions accelerate blade degradation through mechanisms such as leading-edge erosion, which disrupt airflow, increase drag, and decrease lift, ultimately reducing the aerodynamic efficiency and energy output of the turbine. Over time, this degradation alters the aerodynamic characteristics of the turbine, posing
significant challenges to control strategies.
This work addresses this challenge by proposing a physics-informed, learning-based framework for modeling blade degradation in wind turbines. Unlike earlier studies that model degradation as a scalar reduction in performance (typically as a uniform downscaling of the power coefficient CP ), the method developed here decouples the impact on lift and drag coefficients. This approach enables the reconstruction of complex, shape-altering changes in the CP (λ) curve. The result is a generalized degradation framework, parameterized through sensitivity coefficients k1 and k2, that represents the degradation
trajectory in a degradation severity space. These sensitivity coefficients are not static but evolve over time, enabling a dynamic and interpretable representation of aerodynamic degradation.
The framework is implemented into the Wind Speed Estimation - Tip-Speed Ratio (WSE-TSR) control scheme, a closed-loop scheme that is capable of learning. Within this control scheme, the degraded
turbine dynamics are compared with an internal reference model to derive estimation errors, which in turn are used to iteratively update the internal degradation parameters. The result is a correcting controller that learns degradation severity and tracks the evolution of the CP (λ) curve over time.
Initial simulation results, based on modeled degradation paths and a realistic leading-edge erosion case,
revealed limitations in the learning process caused by systematic errors in the control scheme. These errors led to discrepancies between the estimated and actual degraded aerodynamic profiles. However, after identifying and analytically correcting for this model error, the learning algorithm demonstrated significantly improved performance. It was then able to reconstruct accurate degraded CP (λ) curves for moderate and severe degradation scenarios. A discussion is provided on the origin of the learning
inconsistencies and how they could be further investigated. The framework underscores the potential of the framework to support integration into digital twin systems for adaptive control in a degraded state.
By combining physics-based simulation, parameter learning, and a control scheme, this thesis presents a framework that enhances our ability to monitor and interpret degradation in offshore wind turbines. In particular, it provides physics-based insight into how degradation mechanisms, such as leading-edge
erosion, affect aerodynamic performance through changes in lift and drag. This approach contributes to the development of more accurate and adaptive digital twin models that can account for suboptimal aerodynamic performance due to blade degradation.