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.

An extensive set of met mast data offshore northwestern Europe are used to reduce uncertainty in offshore wind speed and turbulence intensity. The performance of widely used industry standard relationships is quantified, while some new empirical relationships are derived for practical application. Motivations include encouraging appropriate convergence of traditionally separate technical disciplines within the rapidly growing offshore wind energy industry.

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.

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 journal paper focuses on developing surrogate models for predicting the damage equivalent load (DEL) on wind turbines without needing extensive aeroelastic simulations. The study emphasizes the development of a sequential machine learning architecture for this purpose. The study also explores implementing simplified wake models and transfer learning to enhance the models' prediction capabilities in various wind conditions.

We explore the effect of blade modifications on offshore wind turbines' performance through a detailed analysis of 12 turbines over 12 years. Introducing the turbine performance integral method, which utilises time-series decomposition that combines various data sources, we uncover how blade wear, repairs and software updates impact efficiency. The findings offer valuable insights into improving wind turbine operations, contributing to the enhancement of renewable energy technologies.

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.

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.

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.

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 explores how wind and power predictions can be improved by introducing local forcing of measurement data in a numerical weather model while taking into account the presence of neighboring wind farms. Practical implications for the wind energy industry include insights for informed offshore wind farm planning and decision-making strategies using open-source models, even under adverse weather conditions.

As longer wind turbine blades are designed, concern about vortex-induced vibration (VIV) grows. This study identifies a new intermittent vortex-shedding behaviour through a long-time simulation of a 3D wind turbine airfoil. This finding motivates a novel evaluation of airfoil vibrations at different inflow velocities. Our results show that both 2D and 3D simulations predict similar VIV characteristics during large motions, enhancing our understanding and prediction of VIV in turbine blades.

Wind turbines are increasing in size and operate more frequently above the atmospheric surface layer, which requires improved inflow models for numerical simulations of turbine interaction. In this work, a novel steady-state model of the atmospheric boundary layer (ABL) is introduced. Numerical wind turbine flow simulations subjected to shallow and tall ABLs are conducted, and the proposed model shows improved performance compared to other state-of-the-art steady-state models.

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.

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 study examines how atmospheric phenomena affect the recovery of wind farm wake – the disturbed air behind turbines. In regions like Oklahoma, where wind farms are often clustered, understanding wake recovery is crucial. We found that wind farms can alter phenomena like low-level jets, which are common in Oklahoma, by deflecting them above the wind farm. As a result, the impact of wakes can be observed up to 1–2 km above ground level.

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.

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.

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 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.

In wind energy, precise wind speed prediction at hub height is vital. Our study in the Dutch North Sea reveals that the on-site-trained random forest model outperforms the global reanalysis data, ERA5, in accuracy and precision. Trained within a 200 km range, the model effectively extends the wind speed vertically but experiences bias. It also outperforms ERA5 corrected with measurements in capturing wind speed variations and fine wind patterns, highlighting its potential for site assessment.

When standing still without a nacelle or blades, the vibrations on a wind turbine tower are of concern to its structural health. This study finds that the air which flows around the tower recirculates behind the tower, forming so-called wakes. These wakes initiate the vibration, and the movement itself causes the vibration to increase or decrease depending on the wind speed. The current study uses a methodology called force partitioning to analyse this in depth.

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.

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.

In a subsidy-free era, there is a need to optimize wind turbines for maximizing farm revenue instead of minimizing cost of energy. A wind-farm-level modeling framework with a simplified market model is used to optimize the turbine size for maximum profitability. The results show that the optimum size is driven mainly by the choice of the economic metric and the market price scenario, with a design optimized for the cost of energy already performing well w.r.t. most profitability-based metrics

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 study investigates dynamic displacement estimation using double-time-integrated acceleration signals for future application in load monitoring based on accelerometers. To estimate displacements without amplitude distortion, a tilt error compensation method for low-frequency vibrations of tower structures using the static bending line without the need for additional sensors is presented. The method is validated using a full-scale onshore wind turbine tower and a terrestrial laser scanner.

The need for renewable energy, thus more efficient wind turbines, is ever increasing. Accurate prediction of the performance in the design stage is necessary. In particular, predicting the dynamic performance of a wind turbine in the region where it undergoes highly unsteady flow is very challenging. We investigated the dynamic performance of an airfoil, which is typical for megastructure wind farms, in support of the development of more efficient design tools in the future.

The US offshore wind industry is growing rapidly. Expansion into cold climates will subject turbines and personnel to hazardous icing. We analyze the 21-year icing risk for US east coast wind areas based on numerical weather prediction simulations and further assess impacts from wind farm wakes over one winter season. Sea spray icing at 10 m can occur up to 67 h per month. However, turbine–atmosphere interactions reduce icing hours within wind plant areas.

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.

Dries Allaerts was born on 19 May 1989 and passed away at his home in Wezemaal, Belgium, on 10 October 2024 after battling cancer. Dries started his wind energy career in 2012 and had a profound impact afterward on the community, in terms of both his scientific realizations and his many friendships and collaborations in the field. His scientific acumen, open spirit of collaboration, positive attitude towards life, and playful and often cheeky sense of humor will be deeply missed by many.

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.

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.

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.

Rain droplets damage wind turbine blades due to the high impact speed at the tip. In this study, it is found that rain droplets and wind turbine blades interact aerodynamically. The rain droplets slow down and deform close to the blade. A model from another field of study was adapted and validated to study this process in detail. This effect reduced the predicted erosion damage by up to 50 %, primarily affecting smaller drops. It is shown how the slowdown effect can influence erosion mitigation.

This paper presents a systematic study of the effect of blade inclination angle, chord distribution, and blade length on vertical-axis wind turbine performance. It shows that, for rotors of identical power production, both blade volume and rotor torque can be significantly reduced through the use of aerodynamically optimised inclined rotor blades. This demonstrates the potential of vertical rotors to reduce the cost of energy for offshore wind when compared to horizontal rotors.

This paper presents a probabilistic cost model for inspection and maintenance (I&M) of turbine support structures in offshore wind farms. It provides the basis for decision-theoretical optimization of I&M strategies and accounts for uncertainties in the costs. The model can be used to optimize I&M strategies at the component, structural system, and wind farm level. Additionally, a simplified method is provided for I&M optimization for individual turbine support structures.

The ability to perform reliable simulations of the motion behaviour of floating offshore wind turbines (FOWTs) is a key requirement for developing resource- and cost-effective designs. To support the development of suitable simulation methods, multiple improvements to the validation process of such methods are presented. These improvements allow, for the first time, the transient aerodynamic loads acting on a FOWT in a wave tank experiment to be directly compared with simulations.

This study emphasizes the need for effective condition monitoring in permanent magnet offshore wind generators to tackle issues like demagnetization and eccentricity. Utilizing a machine learning model and high-resolution measurements, we explore methods of early fault detection. Our findings indicate that flux monitoring with affordable, easy-to-install stray flux sensors with frequency information offers a promising fault detection strategy for large megawatt-scale offshore wind generators.

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.

Offshore wind farms will be built along the East Coast of the United States. Low-level jets (LLJs) – layers of fast winds at low altitudes – also occur here. LLJs provide wind resources and also influence moisture and pollution transport, so it is important to understand how they might change. We develop and validate an automated tool to detect LLJs and compare 1 year of simulations with and without wind farms. Here, we describe LLJ characteristics and how they change with wind farms.

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.

Offshore wind measurements are often affected by gaps. We investigated how these gaps affect wind resource assessments and whether filling them reduces their effect. We find that the effect of gaps on the estimated long-term wind resource is lower than expected and that data gap filling does not significantly change the outcome. These results indicate a need to reduce current wind data availability requirements for offshore measurement campaigns.

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 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.

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.

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

This research integrates custom sensors into wind turbine simulation models for improved performance monitoring utilising a turbine performance integral (TPI) method developed here. Real-world data validation demonstrates that appropriate sensor selection improves wind turbine performance monitoring. This approach addresses the need for precise performance assessments in the evolving wind energy sector, ultimately promoting sustainability and efficiency.

Estimates of power output from regional wind turbine deployments in energy scenarios assume that the impact of the atmospheric feedback on them is minimal. But numerical models show that the impact is large at the proposed scales of future deployment. We show that this impact can be captured by accounting only for the kinetic energy removed by turbines from the atmosphere. This can be easily applied to energy scenarios and leads to more physically representative estimates.