Blade bearings are among the most stressed and challenging components of a wind turbine. Experimental investigations using different test rigs and real-size blade bearings have been able to show that rather short time intervals of only several hours of turbine operation can cause wear damage on the raceways of blade bearings. The proposed methods can be used to assess wear-critical operation conditions and to validate control strategies as well as lubricants for the application.

This study presents the first experimental investigation using two nacelle-mounted wind lidars that reveal the upwind and downwind conditions relative to a full-scale floating wind turbine. We find that in the case of floating wind turbines with small pitch and roll oscillating motions (< 1°), the ambient turbulence is the main driving factor that determines the propagation of the wake characteristics.

Peak wind gust is a crucial meteorological variable for wind farm planning and operations. Unfortunately, many wind farms do not have on-site measurements of it. In this paper, we propose a machine-learning approach (called INTRIGUE, decIsioN-TRee-based wInd GUst Estimation) that utilizes numerous inputs from a public-domain reanalysis dataset, generating long-term, site-specific peak wind gust series.

This research presents the additional benefits of applying an advanced combined wind speed estimator and tip-speed ratio tracking (WSE–TSR) controller compared to the baseline Kω². Using a frequency-domain framework and an optimal calibration procedure, the WSE–TSR tracking control scheme shows a more flexible trade-off between conflicting objectives: power maximisation and load minimisation. Therefore, implementing this controller on large-scale wind turbines will facilitate their operation.

A sensitivity analysis determined the modeling parameters for an operating floating offshore wind turbine with the biggest impact on the ultimate and fatigue loads. The loads were the most sensitive to the standard deviation of the wind speed. Ultimate and fatigue mooring loads were highly sensitive to the current speed; only the fatigue mooring loads were sensitive to wave parameters. The largest platform rotation was the most sensitive to the platform horizontal center of gravity.

Wind energy is one of the pillars to accomplish the future objectives established by governments with regard to the reduction in emissions of CO₂ expected by 2050. Wind energy usage increase will only be possible if more efficient and durable wind turbines are designed. In addition, such increases in wind energy installation worldwide can only be achieved if floating wind turbine design is mature enough. With this purpose a new tool to design and optimize floating wind turbines is presented.

Wind turbine extreme response estimation based on statistical extrapolation necessitates using a small number of simulations to calculate a low exceedance probability. This is a challenging task especially if we require small prediction error. We propose the use of a Gaussian mixture model as it is capable of estimating a low exceedance probability with minor bias error, even with limited simulation data, having flexibility in modeling the distributions of varying response variables.

In standstill conditions wind turbines are at risk of vortex-induced vibrations (VIVs). VIVs can become large and lead to significant fatigue of the wind turbine structure over time. Thus it is important to have tools that can accurately compute this complex phenomenon. This paper studies the sensitivities to the chosen models of computational fluid dynamics (CFD) simulations when modelling VIVs and finds that much care is needed when setting up simulations, especially for specific flow angles.