The paper describes a new approach to democratize access to results from expensive high-performance computer simulations of atmospheric boundary layer flow interacting with wind turbines, in large wind farms. Users interact with the data using a virtual sensor array methodology and essentially stream the data on demand to their analysis or visualization programs rather than having to download files and worrying about data formats, etc.

This research aims to improve wind turbine maintenance by detecting faults early using only data from normal operation. The method analyzes vibration signals in both time and frequency domains and uses a variational autoencoder, a type of deep learning model, to learn normal behavior. It then detects anomalies by measuring how much new data deviate from this learned model. Tests on real turbine data show early and accurate detection of faults such as pitch issues and icing.

We developed a fast, probabilistic surrogate model to predict fatigue loads on floating wind turbines based on site conditions. Unlike traditional methods, our approach directly uses site data, avoiding complex binning or distribution fitting. A mixture density network is used to capture uncertainties and enables quick lifetime fatigue estimates.

Population-based structural health monitoring minimises costs by efficiently sharing information within a wind farm, reducing the need for many sensors and model updates.

This study looks into how changes in wind direction with height (veer) and drivetrain flexibility influence the behavior of large floating wind turbines. Using numerical simulations, it was found that these factors can significantly impact the fatigue damage of the turbines. These results suggest that standardized design methods for modeling wind may underestimate fatigue and that improved modeling could enhance turbine reliability as turbines continue to grow in size.

This study is the first to experimentally test how wind turbine blades behave at near-supersonic speeds, a condition expected in the largest turbines. In the experiments, we observed unstable and potentially detrimental shock waves that become stronger at higher speeds and angles. Basic prediction tools in wind turbine design miss these details, highlighting the need for better tools and experiments to understand the extreme conditions faced by modern wind turbines.

We analyzed the reliability of wind turbine blade bearings under extreme turbulence wind conditions to improve their safety and performance. Using simulations, we studied how factors like turbulence and design variations affect failure probability. Our findings show that current standards may underestimate these risks and highlight the need for stricter controls on critical design parameters.

The Scotian Shelf has world-class offshore wind resources. This study assessed four wind datasets against observations and found notable seasonal and spatial variations in performance. Wind power production decreased substantially in summer and was sensitive to turbine spacing. Dataset uncertainties further increased variability in estimated wind energy output. These findings offer valuable insights for planning future offshore wind farms on the Scotian Shelf.

New designs of large wind turbine blades have become increasingly flexible and thus need cost-efficient monitoring solutions. Hence, we investigate if aerodynamic pressure measurements from a low-cost sensing system can be used to detect structural damage. Our research is based on a wind tunnel study, emulating a simplified wind turbine blade under various conditions. We show that, when using a convolutional-neural-network-based method, structural damage can indeed be detected and its severity can be rated.

This paper presents a frequency-domain dynamics modeling approach for multiple floating wind turbines that are connected by shared mooring lines. It models the wave excitation and response of each floating platform and computes the shared mooring line reactions based on the relative platform motions. A two-turbine scenario demonstrates the approach, and comparison with an established time-domain model verifies its accuracy. The results reveal a new shared mooring tension-dynamics phenomenon.

Floating offshore wind turbines make wind resources in deeper waters accessible. However, these systems are more complex and costly than fixed-bottom wind turbines and require further optimization to reduce cost. This study analyzed the effect of aeroelastic modeling fidelity on the design of the floater and the wind turbine controller. The results showed that an increase in fidelity resulted in lighter platforms, influenced controller parameters and led to lower costs overall.

In this work, a Kriging meta-model of an idling offshore wind turbine is analysed in detail for the first time. It becomes clear that the findings regarding meta-modelling of the idling wind turbine are generally similar to the findings regarding meta-modelling of the same wind turbine in normal operation. However, for the approximation of the rotor blade root bending moments, two additional input parameters have to be included compared to the same wind turbine in operation.

We tested an innovative wind farm concept using novelly engineered wind turbine systems that can guide airflow more efficiently within the farm. Our experiments showed that wind farms deploying this concept can harvest more than twice the wind power per unit area compared to their traditional counterparts. Furthermore, these findings support earlier simulations and point to a more efficient space-saving future for wind energy.