Recent  

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.

This manuscript presents a tow test procedure for measuring the steering behaviour of tethered membrane wings. The experimental setup includes a novel onboard sensor system for measuring the position and orientation of the towed wing, complemented with 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.

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.

As longer wind turbine blades are designed, concern about vortex-induced vibrations (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.

Drivetrain efficiency is a wind turbine’s fundamental behaviour in the energy conversion. To determine the efficiency, input and output powers need to be measured with sufficient accuracy. However, this is a very challenging task due to high complexity and lack of proper calibrated torque measurement. In cooperation with the German and Swiss national metrology institutes, the efficiency determination with state-of-the art instrumentations is demonstrated here with test processes and results.

This study presents the actuator farm model, a new method for modeling wind turbines within large wind farms. The model greatly reduces computational cost when compared to traditional actuator wind turbine models and is beneficial for studying flow around large wind farms as well as the interaction between multiple wind farms. Results obtained from numerical simulations show excellent agreement with past wind turbine models showing its utility for future large-scale wind farm simulations.

In this work, we propose a methodology for evaluating fretting in wind turbine pitch bearing raceways, complemented by a detailed case study. The methodology considers the coupled effects of radial fretting and rotational fretting. The method was previously validated at laboratory and now it is applied to large 4 point contact ball bearings and the 5MW NREL reference turbine. The evaluation reveals critical times for damage initiation and indicate alarmingly short times for fretting initiation.

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 the aero-elastic simulations. The results show small changes in geometry, and aerodynamic and structural loads even for a test case. These findings show the potential to be particularly important for larger and more flexible blades.

Knowing the loads applied to wind turbine gearboxes throughout their service life is becoming increasingly important. Operational deflection shapes identified from fiber-optic strain measurements have enabled the estimation of the gearbox input torque. This allows for future improvements in assessing the remaining useful life. Additionally, tracking the operational deflection shapes over time could enhance condition monitoring in planetary gear stages.

The use of a global base in a proper orthogonal decomposition provides a common base for analyzing flows, such as wind turbine wakes, across an entire parameter space. This can be used to compare flows with different conditions using the same physical interpretation. This work shows the convergence of the global base, its small error compared to the truncation error of 100 modes in the proper orthogonal decomposition, and the insensitivity to which datasets are included for generating it.

A machine learning model is developed using lidar stations around the 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°, 6-hourly resolution from 1987 to 2022 by applying this model to the NOAA/NCEI Blended Seawinds product.

Traditionally, the aerodynamic loss of wind farm efficiency is classified into ‘wake loss’ and ‘farm blockage loss’. This study, using high-fidelity simulations, shows 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.

Our study reveals how different weather patterns influence wind conditions off the U.S. West Coast. We identified key weather patterns affecting wind speeds at potential wind farm sites using advanced machine learning. This research helps improve weather prediction models, making wind energy production more reliable and efficient. 

This paper outlines wind turbine blade repair strategies in India, crucial for its expanding wind energy sector. It discusses challenges, repair techniques for various damages, and the importance of tailored maintenance. Practical cases and monitoring processes are explored, aiming to enhance the reliability of wind turbine blades in the region.

Flow controllers hold promise for boosting wind turbine efficiency by smoothing airflow, optimizing blade angles, and reducing stress on blades. This review examines how different flow controller designs and wind speeds impact turbine performance, paving the way for future advancements in wind energy technology.

Industrial standards for wind turbine design are based on 10-minute statistics of wind speed at turbine hub-height, treating fluctuations as turbulence. But recent work shows the effect of strong transients is described by flow accelerations. We devise a method to measure the accelerations turbines encounter; the extremes offshore defy 10-minute statistics, due to various phenomena beyond turbulence. These findings are translated into a recipe supporting statistical turbine design.

This paper explores an innovative way to boost wind farm efficiency by integrating atmospheric boundary layer control devices with multirotor systems. These devices  speed up the recovery of wind power in wind farm flows. Using both simulations and laboratory experiments, this study shows that the proposed technology can significantly improve power output per land area of wind farms and allow for tighter turbine spacing, potentially leading to more space-efficient and cost-effective wind farms.

The study clarifies the use of probabilistic extrapolation of short/mid-term data for long-term site-specific fatigue assessments. In addition, it assesses the accountability of the Frandsen model in the Lillgrund wind farm as an example of compact layout.