Posters

Abstract

Changing the rated rotor speed of turbines can be used as an operational strategy to reduce structural loading, extend the fatigue lifetime of wind turbines, and comply with noise regulations. However, determining the optimal derating level reliable fatigue-load estimates across a wide range of operating conditions, which becomes computationally expensive when relying solely on full aeroelastic simulations. There is a need for faster methods in applications such as wind-farm layout optimisation and operational control strategy evaluation, where large parameter spaces must be explored efficiently. While surrogate models are often used to approximate fatigue loads, they typically do not consider varying control strategies. To address this challenge, this study introduces a parametric correction model capable of adjusting surrogate fatigue-load predictions to the influence of changing the rated rotor speed. The objective is to provide a computationally efficient and broadly applicable tool for scenario assessment, complementing existing surrogate-based workflows. Detailed aeroelastic simulations remain essential, but the proposed model allows these simulations to be applied later in the process, once candidate strategies have been screened and prioritised. The parametric model is derived from a series of aeroelastic simulations performed using OpenFAST with the ROSCO toolbox. The simulations are conducted on the NREL 5 MW reference turbine across a range of mean wind speeds for different rated rotor speeds. Fatigue responses are evaluated for multiple key sensors: blade-root bending moment, tower-base bending moment, yaw-bearing tilt moment, yaw moment, and low-speed-shaft torque. These simulations form the basis for parametric correction functions, which relate the relative change in fatigue load to the selected derating level. Results show that the fatigue‑load response to rotor‑speed derating is strongly sensor‑dependent. The models can be used to quantify operational trade‑offs between AEP reduction, load reduction and noise reduction. In practical applications, the model enables users to efficiently explore different derating strategies and identify promising operating settings before committing to more detailed simulations. This makes it well suited for fast optimization on both turbine‑level and wind‑farm‑level. The current models consider only variations in mean wind speed and is calibrated using a single reference turbine. Future developments could incorporate additional wind‑condition parameters such as turbulence intensity, shear, and inflow angle, as well as validation across different turbine designs to evaluate the degree of turbine specificity. Despite these limitations, the proposed approach offers an efficient and flexible framework that bridges the gap between simplified load‑estimation tools and full aeroelastic analysis, significantly enhancing the feasibility of operational optimisation involving rotor‑speed derating.