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.

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.

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.

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 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.

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.

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.

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.

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.

As floating wind turbines progress to arrays with multiple units, it becomes important to understand how the wake of a floating turbine affects the performance of other units in the array. Due to the compliance of the floating substructure, the wake of a floating wind turbine may behave differently from that of a fixed turbine. In this work, we present an investigation of the mutual interaction between the motions of floating wind turbines and wakes.

Aeroelastic stability simulations are needed to guarantee the safety and overall robust design of wind turbines. To increase our confidence in these simulations in the future, the sensitivity of the stability analysis with respect to variability in the structural properties of the wind turbine blades is investigated. Multiple state-of-the-art tools are compared and the study shows that even though the tools predict similar stability behavior, the sensitivity might be significantly different.

In our study, we assess how mesoscale weather systems influence wind speed variations and their impact on offshore wind energy production fluctuations. We have observed, for instance, that weather systems originating over land lead to sea wind speed variations. Additionally, we noted that power fluctuations are typically more significant in summer, despite potentially larger winter wind speed variations. These findings are valuable for grid management and optimizing renewable energy deployment.

Wind profilers are important to the wind power industry since they capture wind velocity and direction at higher altitudes than meteorological masts. Although some studies have investigated their performance in different scenarios, this paper covers a gap in knowledge by investigating and comparing their performance under rain events. This investigation is important since the data collected support strategic decisions in the wind power industry, where high data availability in all situations is critical.

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.

A floating wind turbine time domain model, which considers dynamic stall, is used to develop Coleman-free aero-elastic stability analysis methods, namely Hill's and Floquet's. We clarify how the floater tilt is involved in the stability analysis, show damping effects of aerodynamic states, prove that results of both methods agree and can reproduce the forward- and backward-whirling rotor modes in a Coleman-based analysis, and demonstrate that both methods can be applied to a two-bladed rotor.

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.

The paper investigates the influence of the rain drop diameter on the formation of erosion damage and its implications for erosion-safe mode (ESM). By building an erosion damage model that incorporates several drop-size effects, we found that large droplets are significantly more erosive than small droplets. It is shown that the performance of the ESM is significantly increased when drop-size effects are correctly accounted for. A method to derive optimal ESM strategies is given as well.

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.