This study investigates noise annoyance caused by airborne wind energy systems (AWESs), a novel wind energy technology that uses kites to harness high-altitude winds. Through a listening experiment with 75 participants, sharpness was identified as the key factor predicting annoyance. Fixed-wing kites generated more annoyance than soft-wing kites, likely due to their sharper, more tonal sound. The findings can help improve AWESs’ designs, reducing noise-related disturbances for nearby residents.

This paper investigates how tropical cyclones are represented in metocean models and statistics applied to offshore wind design. It provides recommendations for ensuring the accurate representation of extreme waves for design and operation of offshore projects on the Atlantic coast of the USA.

Both fuel cells and combustion engines can be used to supply hydrogen-producing offshore wind turbines with electrical energy in the event of a wind outage. A simulative comparison in combination with a qualitative assessment considering the maritime boundary conditions showed that the combination of an internal combustion engine and a battery storage system is the most suitable for this purpose. This system provides high energy efficiency with the necessary robustness on the high seas.

This research presents an assessment of hurricane risk to offshore wind turbine systems in the Gulf of Mexico. Hurricanes that frequent this area can potentially exceed design limits prescribed by the International Electrotechnical Commission (IEC) wind design standards. Translations between the well-established Saffir-Simpson scale and the IEC design classes were developed to convert to communicate of hurricane severity in terms of design load conditions familiar to wind turbine designers.

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. Eventually this equation can be used to better understand how wind turbines affect the atmosphere.

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.

This study explores how changing wind turbine blade length affects airflow behind the turbine. Using high- and low-fidelity simulations, we found that varying blade lengths accelerate wake interactions but don’t improve flow recovery or energy capture. Turbulence plays a bigger role in breaking up airflow patterns than rotor asymmetry. These findings provide insights for designing more efficient wind turbines.

Maritime authorities often impose turbine alignment constraints on developers to secure the navigation of boats within the wind farm, potentially creating substantial wake losses. The proposed contribution is a new Wind Farm Layout Optimization (WFLO) algorithm that considers these constraints. The proposed method optimizes the grid-alignment parameters and the turbines' location on this grid. We illustrate the algorithm's performances on a challenging example.

We present a wind tunnel study on the effect of an adverse pressure gradient on wakes from porous disks and cylinders. We have quantified the spatial development of the turbulent wakes for Reynolds numbers up to 3.9 × 10⁵ and at distances ranging from 1 to 12 diameters downstream, both with and without a pressure gradient. Consistently with previous studies, we find that the pressure gradient has an effect in all cases, resulting in larger velocity deficits and wider wakes.

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 Discrete Cosine Transform, along with a Long Short-Term Memory model, to predict dynamic responses at a fraction of the computational cost.

Large offshore wind turbines with only two blades possess the potential to reduce the current cost of energy. Yet, this still has to be validated by direct comparisons to a very similar standard three-bladed wind turbine, which is challenging due to the notorious difficulty of establishing equivalent designs of such complex systems. This paper finally presents a methodology to transform a three-bladed turbine into its most similar two-bladed counterpart and provides valuable lessons learned.

A novel wind farm concept, called a regenerative wind farm, is investigated numerically. This concept addresses the significant wake interaction losses among the traditional wind farms. Our results show that regenerative wind farms can greatly reduce these losses, boosting power output per unit land. Unlike traditional farms with 3-bladed wind turbines, regenerative farms use Multi-Rotor Systems with Lifting devices (MRSL), and it is this unconventional design effectively reduces wake losses.

The present work proposes a novel design methodology for increasingly large-diameter offshore wind turbines that includes a measure of cost/fatigue, representing a paradigm shift away from design methods for pure power maximization. Results of the research indicate that notable cost of energy reductions can be achieved for a given wind turbine power rating. The achievable reduction in cost of energy from this novel design methodology will keep wind cost-competitive in the global energy market.

Wind turbines organized in wind farms are an important electric power source. Thereby, each wind turbine causes a so-called wake with a reduced wind speed. This can influence following wind turbines and is an opportunity to optimize the operation of the complete wind farm, i.e., we steer the wakes to increase the total power output of the wind farm. In order to do this efficiently, we exploit repetitive structures within a wind farm.

This study aims to help future large offshore wind turbines and airborne wind energy systems by providing insights into wind speeds at much higher altitudes than previously examined. We assessed three wind models (ERA5, NORA3, and NEWA) to predict wind speeds up to 500 m. Using lidar data from Norway and the North Sea, we found that ERA5 excels offshore, while NORA3 performs best onshore. However, the performance of the models depends on the locations and the evaluation criteria.

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

This paper adds to the literature on rotor disk theory by solving the 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.

Offshore wind farms face the challenge of optimization among many parameters influencing key performance indicators. This research introduces a novel farm optimization framework that enhances flexibility, accuracy, and uncertainty quantification in offshore wind farm design and operation. The proposed work has the key innovation in the ability to translate technical functions into socio-economic objectives, where stakeholders can dynamically weigh these objectives based on their preferences.

This study presents a novel model for predicting wind turbine power output at 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 within a Normal Behavior Model (NBM) framework, the model effectively identifies and analyzes power loss events.

The work explores the potential for wind farm load reduction and power maximization. We carried out a series of high-fidelity wind farm simulations (LES) for a wide variety of atmospheric conditions and operating regimes. Because of turbine-scale interactions and large-scale effects, we observed that the optimal wind farm operating point is reached at lower regimes. Therefore, we proposed three simple approaches with which thrust significantly decreases with only limited impact on power.

We introduce an innovative analytical method to better understand and optimize wind farm performance by accurately calculating how turbine wakes affect each other. Unlike traditional numerical approaches, our method provides a precise way to measure the impact of upstream wakes on downstream turbines. This new approach, validated through numerical comparisons, enhances optimisation strategies, potentially leading to more efficient wind farm operations and increased power generation.

Lidars are vastly used in wind energy but most users struggle when interpreting lidar turbulence measures. Here we explain why is difficult to convert them into standard measurements. We show two ways to convert lidar to in-situ turbulence measurements, both using neural networks with one of them based on physics while the other is purely data driven. They show promising results when compared to high-quality turbulence measurements from a tall mast.

Wind farms play an important role in the energy transition. Unfortunately, the power production of wind farms can fluctuate heavily and depends on many parameters. It is, however, crucial that there is always an equilibrium between electricity production and consumption. Therefore it is important to have accurate power forecasts. This paper presents a methodology, based on machine learning, to generate better farm power forecasts, enabling better scheduling, trading and balancing of wind energy.

In this paper, we propose a novel machine-learning framework pitch misalignment detection in wind turbines. By using a minimal set of standard sensors, our method detects misalignments as small as 0.1deg 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

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 one day in advance, helping manage wind power more efficiently for a stable, reliable energy supply.

The interaction of the wake of a wind turbine with neighboring and downstream turbines in a wind farm can have a significant impact on both energy capture and unsteady turbine loads. The understanding the fluid-dynamic behaviour in the wake therefore has a plethora of important applications. The development of efficient, user-friendly numerical tools to investigate these phenomena is therefore critical to the future simulation of wind farms. In this work, an efficient flow solver is presented.

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.

The use of flying wings in AWES 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 identified. It is analyzed in terms of controllability at different wind speeds and guidance parameters. Based on this, a flight regime is identified and a controller is designed. Simulation results show that the control concept facilitates the desired launching and landing.

This paper reports the mitigation of power system oscillations by using power system stabilizer aided TCSC. For analyzing the performance of the wind-farm integrated test system, various parameters (line power, bus voltages, rotor angle deviation, active & re-active power) of the test system are observed under random speed variation with different types of faults.

This paper explores potential technical enhancements to wind lidar profilers for improved turbulence measurement. The study separately tests the effects of increased sampling rate and reduced probe length against a commercial lidar of the same type. Various turbulence metrics were quantified to evaluate the impact of these technical modifications. The results indicate that increasing the sampling rate is the most valuable enhancement for turbulence measurement.

This research develops a new method for assessing Hybrid Power Plants (HPPs) 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.

The paper aims to study the far-wake of a wind turbine under realistic inflow conditions subjected to harmonic floating motions. The present work show that phase-averaging enables the observation of the coherent spatio-temporal wake behaviour in response to the harmonic motions, on contrary to previous studies with time-averaging, and that the resulting variations of the chosen metrics exhibit an intensity higher than those expected by using basic quasi-steady-state approaches.