This paper reviews the technical behaviour defined for a specific control method, grid-forming control, used in inverter-integrated power generation sources such as wind power plants, solar power plants, and battery energy storage systems. Considering the growing trend of offshore wind power plants, the paper adapts the behaviours into offshore wind power applications; re-classifies them into mandatory, optional, and advanced categories; and provides testing methods to assess these behaviours.

Noise from wind turbines has been contentious for many years for those who have to live near wind farms. Despite strict regulations and significant research into the subject, it continues to be an issue for wind energy's role in the green transition. We were curious to find out why. Our research into three research ("epistemic") communities shows that noise is not understood as the same thing by the scientists dealing with it, fuelling the controversies over the solutions proposed.

The use of flying wings in airborne wind energy systems (AWESs) is promising. This paper develops a guidance concept for launching and landing of such a flying wing AWES. The fundamental challenges are presented, and a suitable guidance concept is identified. It is analyzed in terms of controllability at different wind speeds and guidance parameters allowing the design of a controller. Simulation results show that the guidance concept presented here facilitates the desired launching and landing.

Atmospheric large-eddy simulation is a technique that simulates weather conditions in high detail and is used to plan new wind farms. This research presents ways to estimate the long-term (10-year) power production of a wind farm without having to simulate 10 years of weather and instead simulating much less (1 year or less). The results show that the methods reduce the uncertainty in power production estimates by an order of magnitude and that wind observations can be included as well to add more insight.

Two approximate analytical expressions are derived, one for the variance of wind speed and the other for turbulence intensity, based on one simple assumption: that the turbulent fluctuations in wind are small with respect to the mean. The formulations perform well when applied to the observations from the American WAKE experimeNt (AWAKEN) field campaign conducted in 2023.

The structures at the center of wind turbine rotors are loaded by three rotor blades. The rotor blades have different loads, which depend on their positions and the incoming wind. The number of possible different loads is too high to simulate each of them for later design of the structures. This work attempts to reduce the number of necessary simulations by exploring inherent relations between the loads of the three rotor blades.

The amount of energy that can be extracted from wind depends primarily on the blade geometry, which can be affected by elastic deformations. This paper presents a first study analysing the influence of cross-sectional deformations of a 15 MW wind turbine blade on aero-elastic simulations. The results show that cross-sectional deformations have a minor influence on the internal loads of rotor blades in normal operation.

This study is the first to use real-world atmospheric measurements to show that large wind plants can increase the height of the planetary boundary layer, the part of the atmosphere near the surface where life takes place. The planetary boundary layer height governs processes like pollutant transport and cloud formation and is a key parameter for modeling the atmosphere. The results of this study provide important insights into interactions between wind plants and their local environment.

This paper investigates the treatment of tropical-cyclone-generated waves in metocean models and in statistical evaluation for offshore wind design. It provides recommendations for ensuring the accurate representation of extreme waves for the design and operation of offshore projects on the Atlantic coast of the United States.

This work presents a small-scale machine capable of flying delta kites with three lines for airborne wind energy research, that is, the extraction of energy from high-altitude winds using aircraft. Its built-in controller allows it to operate autonomously, demonstrated by two test flights that are presented and compared. Additionally, the results obtained by the analysis of the data will help to further improve the control and better understand the behavior of these devices.

This research develops a new method for assessing hybrid power plant (HPP) profitability, combining wind and battery systems. It addresses the need for an efficient, accurate, and comprehensive operational model by approximating a state-of-the-art energy management system (EMS) for spot market power bidding using machine learning. The approach significantly reduces computational demands while maintaining high accuracy. It thus opens new possibilities in terms of optimizing the design of HPPs.

Unexpected load events measured on operating wind turbines are not accurately predicted by numerical simulations. We introduce the periods of constant wind speed as a possible cause of such events. We measure and characterize their statistics from atmospheric data. Further comparisons to standard modelled data and experimental turbulence data suggest that such events are not intrinsic to small-scale turbulence and are not accurately described by current standard wind models.

This paper presents a fast cycle–power computation model for fixed-wing ground-generation airborne wind energy systems. It is suitable for sensitivity and scalability studies, which makes it a valuable tool for design and innovation trade-offs. It is also suitable for integration with cost models and systems engineering tools, enhancing its applicability in assessing the potential of airborne wind energy in the broader energy system.

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.

Offshore wind farms along the US East Coast can have limited effects on local weather. To study these effects, we include wind farms near Massachusetts and Rhode Island, and we test different amounts of turbulence in our model. We analyze changes in wind, temperature, and turbulence. Simulated effects on surface temperature and turbulence change depending on how much turbulence is added to the model. The extent of the wind farm wake depends on how deep the atmospheric boundary layer is.

Wind turbines add turbulence to the atmosphere behind them, especially 4–6 diameters downstream and near the rotor top. We propose an equation that predicts the distribution of added turbulence as a function of a turbine parameter (thrust coefficient) and an atmospheric parameter (undisturbed turbulence intensity before the turbine). We find that our equation performs well, although not perfectly. Ultimately this equation can be used to better understand how wind turbines affect the atmosphere.

A methodology for evaluating fretting in wind turbine pitch bearing raceways is presented, and a case study is evaluated. The methodology considers the coupled effects of radial fretting and rotational fretting. As result, critical locations based on the dissipated energy are identified for the different wind speeds and the contributions of operational and non-operational times, as well as the prediction of damage shapes on the raceway for both cases, are evaluated independently and compared.

This study investigates how wind turbine blades damaged by erosion, along with changing wind conditions, affect power output. Even minor blade damage can lead to significant energy losses, especially in turbulent winds. Using simulations, it was discovered that standard power data analysis methods, including time averaging, can hide these losses. This research highlights the need for better blade damage detection and careful wind data analysis to optimise wind farm performance.

This article presents a tow test procedure for measuring the steering behaviour of tethered membrane wings. The experimental set-up includes a novel onboard sensor system for measuring the position and orientation of the towed wing, complemented by an attached low-cost multi-hole probe for measuring the relative flow velocity vector at the wing. The measured data (steering gain and dead time) can be used to improve kite models and simulate the operation of airborne wind energy systems.

The rising share of wind power poses challenges to cost-effective integration while ensuring reliability. Balancing the needs of the power system and contributions of wind power is crucial for long-term value. Research should prioritize wind power advantages over competitors, focussing on internal challenges. Collaboration with other technologies is essential for addressing the fundamental objectives of power systems – maintaining reliable supply–demand balance at the lowest cost.

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.

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.

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).

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.

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.

Mitigating turbine wakes is an important aspect to maximizing wind farm energy production but is a challenge to model. We demonstrate a new approach to modeling active wake mixing, which re-energizes turbine wake through periodic blade pitching. The new model divides the wake into separate steady, unsteady, and turbulent components and solves for each in a computationally efficient manner. Our results show that the model can reasonably predict the faster wake recovery due to mixing.

The document presents an experimental study on the parked loads of floating vertical axis wind turbines (VAWTs) in a wind and wave basin, focusing on the effects of wind speed, solidity, and floating platform dynamics. Findings show that higher wind speed and higher solidity generally increase the parked loads, while a floating platform introduces additional effects due to tilting. A semi-numerical model was also presented to predict the parked loads, which helps enhance VAWT design.

A machine learning model is developed using lidar stations around US coasts to extrapolate wind speed profiles up to the hub heights of wind turbines from surface wind speeds. Independent validation shows that our model vastly outperforms traditional methods for vertical wind extrapolation. We produce a new long-term gridded dataset of wind speed profiles from 20 to 200 m at 0.25° and 6-hourly resolution from 1987 to the present by applying this model to the National Oceanic and Atmospheric Administration (NOAA)/National Centers for Environmental Information (NCEI) Blended Seawinds product.

Wind farms can be subject to rapidly changing weather events. In the United States Great Plains, some of these weather events can result in waves in the atmosphere that ultimately affect how much power a wind farm can produce. We modeled a specific event of waves observed in Oklahoma. We determined how to accurately model the event and analyzed how it affected a wind farm’s power production, finding that the waves both decreased power and made it more variable.

This study analyses wind speed data at heights up to 500 m to support the design of future large offshore wind turbines and airborne wind energy systems.  We compared three wind models (ERA5, NORA3, and NEWA) with lidar measurements at five sites using four performance metrics. ERA5 and NORA3 performed equally well offshore, with NORA3 typically outperforming the other two models onshore. More generally, the optimal choice of model depends on site, altitude, and evaluation criteria.

This paper presents a methodology for assessing the system design and scaling trends in airborne wind energy (AWE). A multi-disciplinary design, analysis, and optimisation (MDAO) framework was developed, integrating power, energy production, and cost models for the fixed-wing ground-generation (GG) AWE concept. Using the levelized cost of electricity (LCoE) as the design objective, we found that the optimal size of systems lies between the rated power of 100 and 1000 kW.

In this paper, we propose a novel machine-learning framework for pitch misalignment detection in wind turbines. Using a minimal set of standard sensors, our method detects misalignments as small as 0.1° and localizes the affected blades. It combines signal processing with a hierarchical classification structure and linear regression for precise severity quantification. Evaluation results validate the approach, showing notable accuracy in misalignment classification, regression, and localization.

For wind energy in the Arctic, a better understanding of wind characteristics is necessary. Observations from a valley in Svalbard show that local, thermally driven valley winds are frequent. They increase the average wind speed and reduce the wind variability in this valley, which is not reproduced well by common numerical model products. The wind characteristics are advantageous for wind power in valleys.

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.

This paper presents one half of a companion paper series that studies strategies to reduce negative aerodynamic interference (i.e., wake effects) between nearby wind turbines in a wind farm. The approach leverages high-fidelity flow simulations of an open-source design for a wind turbine. Complimenting the companion paper’s analysis of the power and loading effects of the wake-control strategies, this article uncovers the underlying fluid-dynamic causes for these effects.