We introduced a new method to monitor wind turbine gearboxes using distributed fiber-optic technology. By attaching a single optical fiber to the gearbox, we tracked strain changes at high resolution, capturing gear movement, torque, and temperature in real time. This study demonstrated the link between torque and strain, enabling early fault detection. The approach offers a cost-effective way to improve gearbox reliability and reduce downtime, supporting more sustainable wind energy operations.

This study explores how changing wind turbine blade length affects the aerodynamics behind the turbine. Using high-fidelity simulations, we found that varying blade lengths accelerates the leapfrogging event but does not improve wake recovery directly. In contrast, turbulence plays a bigger role, as the wake breakdown process is more influenced by it over the studied rotor asymmetries. These findings provide insights for designing more efficient wind turbine rotors.

Both fuel cells and combustion engines can be used to supply hydrogen-producing offshore wind turbines with electrical energy in the event of calm wind. A simulative comparison in combination with a qualitative assessment considering the maritime boundary conditions showed that the combination of an internal combustion engine and a battery storage system is the most suitable for this purpose. This system provides high energy efficiency with the necessary robustness on the high seas.

A parametric model for the wind direction rose is presented, with testing on real offshore wind farm data indicating  that the model performs well. The presented model provides opportunities for standardisation and enables more systematic analyses of wind direction distribution impacts and sensitivities.

We studied the airflow around a new type of wind turbine called the X-Rotor, which could help to reduce the cost of offshore wind energy. Comparing a computer simulation model and wind tunnel experiments, we found that the model correlates well under normal conditions but becomes less accurate when the blades pitch. Our results show that future designs of this turbine category must consider complex 3D flow effects to better predict and improve wind turbine performance.

The offshore wind farm sector is expanding rapidly, and the interactions between wind farms are important to analyse for both existing and planned wind farms. We developed a new methodology to quantify how much the reductions in wind speed behind a farm can affect the loads on turbines which are tens of kilometres downstream. We find a 2.5 % increase in the turbine loads and discuss how further measurements could add to the design standards of future wind farms.

This study evaluates the impact of an enhanced sampling rate on turbulence measurements using the Vaisala WindCube v2.1 lidar profiler. A prototype configuration, sampling 4 times faster than the commercial setup, is compared to the commercial configuration, with reference measurements provided by a 2D sonic anemometer. The prototype lidar captures greater variance, resulting in turbulence estimates that are more closely aligned with the reference.

Weather simulations carried out over a decade showed that the average erosivity of rainfall on wind turbine blades increases from the southwestern part of the Dutch North Sea to the northeastern region. These results suggest that future wind farms developed in the northeast are likely to encounter higher erosion rates compared to those currently operating in the southwest. This requires special attention when developing mitigation strategies.

We explore the application of a novel wind turbine control methodology for maximizing power generation while also improving the turbine's lifespan by reducing fatigue load on the tower structure, blades, and rotor. Simulations of the controller against a state-of-the-art baseline controller and an existing lidar-assisted control method on a 15 MW reference turbine showed that the controller improves on all performance metrics without sacrificing power generation or tracking performance.

A 3-year meteorological dataset from an operational wind farm of six 2 MW (megawatt) turbines has been made available. This includes a meteorological mast equipped with sonic anemometers at four different heights and radiometer measurements for atmospheric stability analysis. Simultaneously, supervisory control and data acquisition (SCADA) and the scanned geometry of the turbine blades are provided. This database has been made accessible to the research community (https://awit.aeris-data.fr).

Increasing the efficiency of wind farms can be achieved via reducing the impact of wakes – flow regions with lower wind speed occurring downwind from turbines. This work describes training and validation of a novel method for the estimation of the wake effects impacting a turbine. The results show that for most tested wind conditions, the developed model is capable of robust detection of wake presence and accurate characterisation of its properties. Further validation and improvements are planned.

A hybrid fault detection method is proposed, which combines physical domain knowledge with machine learning models to automatically detect mechanical faults in wind turbine drivetrain components. It offers detailed insights for experts while giving operators a high-level overview of the machine's health to assist in planning effective maintenance strategies. It was validated on multiple years of wind farm data and the potential faults were accurately predicted, which was confirmed by experts.

Site-specific performance analysis of wind turbines is crucial but computationally prohibitive due to the high cost of evaluating numerical models. To address this, the authors propose a machine learning model combined with dimensionality reduction using principal component analysis and the discrete cosine transform, along with a long short-term memory model, to predict dynamic responses at a fraction of the computational cost.

The variation in wind speed over the rotor plane causes oscillations in the blade, leading to fatigue damage. These oscillations can be reduced with individual pitch control (IPC), at the expense of more blade actuation. This work proposes two output-constrained IPC methods to facilitate the trade-off between load reduction and actuation increase. Both methods can smoothly transition between conventional full IPC action and no IPC action.

This study compares a wind turbine with blades behind the tower (downwind) to the traditional upwind design. Testing a 1.5 MW turbine at the National Renewable Energy Laboratory's Flatirons Campus, we measured performance, loads, and noise. Numerical models matched well with observations. The downwind setup showed higher fatigue loads and sound variations but also an unexpected power improvement. Downwind rotors might be a valid alternative for future floating offshore wind applications.

This paper studies the instantaneous centre of rotation (ICR) of floating offshore wind turbines (FOWTs). We present a method for computing the ICR and examine the correlations between the external loading, design features, ICR statistics, motions, and loads. We demonstrate how to apply the new insights to successfully modify the designs of the spar and semisubmersible FOWTs to reduce the loads in the moorings, the tower, and the blades, improving the ultimate strength and fatigue properties.

We study how flow blockage improves wind-farm efficiency using large-eddy simulations and develop an analytical model to better predict turbine power under blockage. We find that blockage enhances turbine power and thrust by creating a favourable pressure drop across the row, reducing near-wake deficit while inducing an unfavourable pressure increase downstream, which has minimal direct impact on far-wake development.