Wind energy is expanding into regions exposed to tropical cyclones, where extreme winds and waves can damage turbines and disrupt energy production. We reviewed observations, simulations, engineering standards, and risk quantification frameworks to identify the most important gaps. Today's design rules, originally built for milder storms, do not capture cyclone hazards. Closing these gaps requires better offshore measurements, improved models, and risk-informed design standards.

Wind turbines interact with the atmosphere and affect the performance of nearby turbines. Using a large dataset from a wind farm in northern Germany, we studied how inflow conditions shape the flow behind a turbine, called a wake. Higher turbulence reduces the wake's length and impact, while stable conditions make it stronger. Wind direction changes with height can shift and skew the wake’s position. These findings help improve turbine wake models by considering varying atmospheric conditions.

Wind farm flow control mitigates wake effects within a wind farm by adjusting the turbine settings to improve the overall farm performance. This study quantifies the value of a combined strategy in which each turbine can apply wake steering or the helix wake mixing method. The proposed method is simulated for a large-scale wind farm, for which such a combined strategy provides higher gains in annual energy production compared to the individual techniques.

Wind turbines create wakes, which reduce downstream power. Optimizing turbine placement requires accounting for these reductions. We compared a neural network trained on numerical simulations against engineering wake models across various farm sizes. The neural network predicted flow most accurately but was slower. Surprisingly, a simple TurbOPark model produced layouts with higher validated energy output, suggesting that accuracy is not the only important metric for such models.

As offshore wind farms are built closer together, predicting how they affect each other becomes critical. We compared two AI approaches for this task, training both on cheap approximate data before refining them with expensive high-accuracy simulations. One predicts wake boundaries better, while the other estimates wind speeds more accurately, offering complementary tools for future wind farm design.

We conducted wind tunnel experiments on nine wind turbine models and measured their power while intentionally yawing them away from the wind direction. By testing a broad range of yaw-angle combinations, we found that this method can increase the total power output by up to 5.3%. We also observed how different columns of turbines respond uniquely to these changes and how wind speed affects the overall improvement. Our findings could be useful in developing more accurate wind farm control models.

A comparison between the power generated by a 5 MW turbine mounted on a tension-leg platform and subjected to fully developed ocean wave movements, and the same platform/turbine not subjected to ocean motions shows that these wave motions have little effect on time-average power output over a large wind speed range.

A multi-parametric panel approach, integrated into a wind turbine blade optimization workflow, is provided. The formulation enables sandwich panels to be represented as modular assemblies with independently parameterized materials. This capability enabled the examination of Double-Double laminates as viable replacements for conventional triaxial skins in a large-scale optimization, like demonstrated for a large blade. The results show a mass reduction without changing the production process.

This research presents the first use of the simplified metastatistical extreme value approach for wind extremes, extending it to wind energy applications. We use a categorical framework that combines climate, roughness, and topography to evaluate convection-permitted models. We find that model formulation drives inter-model uncertainties, rather than surface conditions. Convection-permitted models' uncertainty analysis improves the reliability of extreme wind estimates for design parameters. 

A new flight control architecture for airborne wind energy systems is proposed for circular-pattern flight during the power production phase. The controller 'wraps' around a reference cylinder and thereby does not require exact path planning or waypoint-based navigation. This simple guidance method enables the controller to function with only proportional-integral regulators. Further extensions to the controller enable flying with near-constant angle of attack and multi-kite synchronisation.

This study introduces a new way to manage floating offshore wind farms by combining different control approaches into one integrated system. Acting like a central brain, it decides how turbines should work together to capture more energy or follow grid demands. Simulations show that this integrated method outperforms individual approaches, making offshore wind power more efficient, flexible, and reliable.

This study shows how different modeling assumptions made in discrete-event simulation models can change predictions of maintenance costs and power losses for floating offshore wind farms. By testing two models under the same conditions, we identify which assumptions matter most and how they shape results. The findings help improve the reliability of future models and support better planning of maintenance strategies for floating-wind projects.

Two major aircraft measurement campaigns above the North Sea provide insights into modifications of the wind field and sea surface induced by wind farms. The aircraft performed transects at hub height upstream and downstream of wind farm clusters and identified different effects, e.g. how long it takes for the wind speed to recover after the wind farm, how changes across the coastline interact with wind energy, and if wind farms are well represented in numerical simulations.

Bat protection in wind farms can require temporarily stopping turbines, reducing energy production and affecting structural loading. We developed a data-driven method combining operational records, bat activity measurements, wind-farm flow modeling, and load models to estimate production impacts and long-term fatigue. In a French onshore case study, event-triggered curtailment reduced production losses relative to fixed schedules, while load impacts were small and depended on wind conditions.

Predicting wind farm energy production is challenging because wind patterns are complex. We tested 16 different models against real data from a major field experiment to see which worked best. Surprisingly, the most expensive and detailed models were not always more accurate than simpler ones. We found that feeding models better weather data was the most effective way to improve accuracy. These results help the industry choose the right tools for designing more-efficient wind farms.