This paper adds to the literature on rotor disk theory by solving a century-old problem with a different mathematical approach. The classical results are recovered, while also adding expressions and asymptotic limits for performance coefficients that were not included in the original theory. This work adds to classical rotor disk theory as a valuable contribution to research and teaching in wind turbine aerodynamics.

In this study, we assessed the total quantity of microplastics emitted by wind turbines currently operating in the Dutch North Sea. The estimates of microplastics currently emitted from offshore wind turbines in the Netherlands account for a very small portion of the total microplastics released offshore in the Netherlands, specifically less than 1 ‰.

This study demonstrates how kites used to generate wind energy can act as sensors to measure wind conditions and system behaviour. By combining data from existing sensors, such as those measuring position, speed, and forces on the tether, a sensor fusion technique accurately estimates wind conditions and kite performance. This approach can be integrated into control systems to help optimize energy generation and enhance the reliability of these systems under changing wind conditions.

This study presents a novel model for predicting wind turbine power output at a high temporal resolution in wind farms using a hybrid graph neural network (GNN) and long short-term memory (LSTM) architecture. By modeling the wind farm as a graph, the model captures both spatial and temporal dynamics, outperforming traditional power curve methods. Integrated with a normal behavior model (NBM) framework, the model effectively identifies and analyzes power loss events.

The wake of a scaled vertical-axis wind turbine farm was measured, resulting in the first experimental database of 3D-resolved flow-field measurements. In addition to the baseline operating conditions, two modes of wake control were tested, which involve the passive adjustment of the rotor blade pitch. The results highlight the impacts of these mode adjustments on the trailing vorticity system, wake topology, and affinity towards increasing the rate of wake recovery throughout the farm.

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.

Traditionally, the aerodynamic loss of wind farm efficiency is classified into wake loss and farm blockage loss. This study, using high-fidelity simulations, shows that neither of these two losses is well correlated with the overall farm efficiency. We propose new measures called turbine-scale efficiency and farm-scale efficiency to better describe turbine–wake effects and farm–atmosphere interactions. This study suggests the importance of better modelling farm-scale loss in future studies.

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.

We investigate asymmetries in terms of power performance and fatigue loading on a five-turbine wind farm subject to wake steering strategies. Both the yaw misalignment angle and the wind direction were varied from negative to positive. We highlight conditions in which fatigue loading is lower while still maintaining good power gains and show that a partial wake is the source of the asymmetries observed. We provide recommendations in terms of yaw misalignment angles for a given wind direction.

It is crucial to understand the loads and power production of wind turbines under different atmospheric situations (e.g., night and day changes). Computational simulations are a widely used tool to get more knowledge of the performance and the wake of wind turbines. In this study realistic velocity profiles of the atmosphere are used as input for simulations so that these simulations become more realistic. The generated flow is used as inflow for wind turbine simulations.

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.

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.

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.

Large wind turbines face design challenges due to increased flexibility of blades. Conventional control strategies fail under large deformations, impacting performance. We present a feedforward–feedback control scheme, addressing flexibility and overcoming the limitations of conventional strategies. By testing it on a large-scale reference turbine with realistic wind conditions, we demonstrated improvements to power by up to 5 % while constraining blade deflections.

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.

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.

In this study, we compare turbulence intensity measurements from two buoy-mounted wind lidars with data from a fixed lidar and a meteorological mast. Turbulence intensity is essential for understanding wind conditions but is often overestimated by floating systems due to wave motion. We applied a physics-based compensation to reduce these effects. Our findings show that motion compensation significantly improves accuracy, making floating lidar systems suitable for offshore wind site assessments.

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.

Our study explores how frontal low-level jets (FLLJs) impact wind power production by causing ramp-down events. Using the Weather Research and Forecasting model, we analyzed various modeling configurations and found that initial and boundary conditions, domain configuration, and wind farm parameterization significantly influence simulations. Our findings show such extreme events can be forecasted 1 d in advance, helping manage wind power more efficiently for a stable, reliable energy supply.

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.

Waves interact with the overlying wind field by modifying the stresses at the atmosphere–ocean interface. We develop and employ a parameterization method of wave-induced stresses in the numerical simulation of an offshore wind farm in a stable atmospheric boundary layer. This work demonstrates how swells change the kinetic energy transport and induce wind veer and wake deflection, leading to significant variations in the power output of wind turbines at different positions of the wind farm.

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.

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.

Three recent wind resource datasets are assessed for their skills in representing annual average wind speeds and seasonal, diurnal, and interannual trends in the wind resource in coastal locations to support customers interested in small and midsize wind energy.

This research estimates the environmental impact of New Jersey offshore wind under a domestic supply chain using life cycle assessment modelling.

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 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.

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.

This work employs deep neural networks to identify damage in the mooring system of a floating offshore wind turbine using measurements from the platform response. We account for the effect of uncertainty caused by the existence of multiple solutions using a Gaussian mixture model to describe the damage condition estimates. The results reveal the capability of the methodology to discover the uncertainty in the assessment, which increases as the instrumentation system becomes more limited.

Wind farms are crucial for a sustainable energy future. However, their power can fluctuate significantly due to changing weather conditions, which complexly affect their power generation. This paper presents a novel machine-learning-based method to enhance wind farm power predictions, enabling improved power scheduling, trading and grid balancing. This makes wind power more valuable and easier to integrate into the energy system.

This paper investigates the impact of large movements allowed by floating wind turbine foundations on their aerodynamics and wake behavior. Wind tunnel tests with a model turbine reveal that platform motions affect wake patterns and turbulence levels. Insights from these experiments are crucial for optimizing large-scale floating wind farms. The dataset obtained from the experiment is published and can aid in developing simulation tools for floating wind turbines.

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.

This paper proposes a data-driven approach to simulate wind turbine sensor time series, such as temperature and pressure signals, describing the behaviour of a wind turbine component as it degrades through time up to the failure point. It allows for the simulation of new failure events or the replication of a given failure under different conditions. The results show that the synthetic signals generated using this approach improve the performance of fault detection and prognosis methods.

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

Wind farms suffer energy losses due to wake effects between turbines. We present a new control strategy that synchronizes turbine wakes to enhance power output. By estimating and aligning the phase shifts of periodic wake structures using an advanced filtering method, downstream turbines recover more energy. Simulations show up to 10 % increased power at the third turbine. These results offer a promising direction for improving wind farm efficiency while mixing wakes.

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.

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.

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.

Wind turbine rotor blades consist of subcomponents that are glued together. Such connections are subject to fatigue loads. This paper analyzes the fatigue load characteristics of three different wind turbine rotor blades in trailing edge adhesive joints. It is shown that the fatigue loads have measurable degrees of non-proportionality and that the choice of the procedure to calculate the fatigue damage is crucial for designing reliable blades.

The paper discusses how to calculate the life of a blade bearing that is a roller bearing, as opposed to ball bearings, which most papers on the subject discuss. The raceway fatigue life of the bearing is calculated in a very detailed manner. This includes a validated finite-element simulation model and an approach to determine loads for all operating conditions that the wind turbine experiences.

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.

We explore how simple terrain influences spatial variations in wind speed and wind farm performance during a low-level jet. Using simulations, field observations, and turbine production data, we find that downstream turbines produce more power than upstream ones, despite being subjected to wake effects. This counterintuitive result arises because the low-level jet and winds near turbine rotors are highly sensitive to topographic features, leading to stronger winds at the downstream turbines.

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.

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.

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.

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.

Numerical models, used to assess loads on floating offshore wind turbines, require many input parameters to describe air and water conditions, system properties, and load calculations. All parameters have some possible range, due to uncertainty and/or variations with time. The selected values can have important effects on the uncertainty in the resulting loads. This work identifies the input parameters that have the most impact on ultimate and fatigue loads for extreme storm load cases.

The work explores the potential for wind farm load reduction and power maximization. We carried out a series of high-fidelity large-eddy simulations for a wide range of atmospheric conditions and operating regimes. Because of turbine-scale interactions and large-scale effects, we observed that maximum power extraction is achieved at regimes lower than the Betz operating point. Thus, we proposed three simple approaches with which thrust significantly decreases with only a limited impact on power.