Large Wind Research Articles

Analysis of the Vibration Effect on the Trailing Edge Noise for Wind Turbine Applications

The noise emitted is a factor in determining the location for installing wind turbines. It can be a concern for people living near wind farms due to the nuisance and health problems caused by noise emissions. Therefore, noise estimation is one of the most relevant aspects of wind turbine design. Aerodynamic noise can be caused by air motion around the turbine blades. This can create a swooshing or whooshing sound as the blades pass through the air. Aerodynamic noise tends to be more prominent at higher wind speeds when the turbine is operating at full capacity. The trailing edge noise is the main source of wind turbine aerodynamic noise. In this work, semi-analytical solutions for modeling this effect are used. The Ffowcs Williams and Hawkings (FW-H) acoustic analogy is used to predict far-field noise generated by wind turbine blades moving in the air. The equations of the rotor–tower–nacelle structure are obtained by the classical finite element method for a typical onshore wind turbine to predict the blade vibration signal. This new formulation is applied to rotating sources and statistical analysis of broadband trailing edge noise. The novelty of this work is to model the acoustic–structure interaction between the wind flow and the blade structure vibration. In this context, the acoustic far-field pressure emissions are modified by source vibration. In this point, this work compares the acoustic pressure values from rigid and flexible sources. Once the relationship between acoustic emission and wind turbine structural vibration is determined, the potential application of this research is, for example, to provide valuable information about the health and integrity of the blades. Thus, the goal of this work is to predict the aerodynamic noise of a typical large wind turbine taking into account its structural behavior. Therefore, Formulation 1A is used to calculate the far-field noise radiated from the trailing edge of wind turbine blades in a low Mach number flow. The vibration effects are input variables to evaluate the intensity and acoustic pressure values. Some computational aspects are discussed and the numerical model is clarified. The acoustic predictions are validated with analytical results and experimental measurements that are available in the scientific literature. The numerical results of the FW-H acoustic analogy demonstrate that the aerodynamic noise value is predominantly low frequency in the sections closest to the source.

Large offshore wind farms have minimal direct impacts on air quality

Abstract Wind power has rapidly grown over the past decade because it is clean, renewable, and abundant. However, wind farms can affect local weather and possibly alter the transport, diffusion, and concentration of air pollutants. Given the unprecedented expansion of offshore wind farms planned along the U.S. East Coast by the Bureau of Ocean Energy Management (BOEM), this study aims to investigate if and how those future offshore wind farms might directly affect air pollution along the densely populated East Coast, in particular, the concentrations of ozone (O3), fine particulate matter (PM2.5), sulfur dioxide (SO2), and nitrogen dioxide (NO2). These pollutants are regulated at the federal and state levels and are harmful to human health. We exclusively study the direct effects of the wind turbines on air pollution (via meteorological changes), rather than investigating the indirect impacts of replacing fossil-fuel power plants with wind farms. We first run a numerical meteorological model, the Weather Research and Forecast (WRF) model, to simulate the meteorology along the U.S. East Coast during the summer of 2018 in two scenarios, with and without the wind farms. Then we use the output of these two sets of simulations from the WRF model as input to the Comprehensive Air Quality Model with extensions (CAMx) to simulate the changes in air quality in the study domain due to the wind farms. On average, we find a minor increase in O3 levels within the wake of the New Jersey WEA. The minor changes to O3 can be attributed to the slight temperature increase below the turbine hub height, within the rotor area, as well as a significant decrease in wind velocity in the wake of the turbines and a slight increase in VOCs. In addition, we report that the other three pollutants remain unchanged in the presence of wind farms. In summary, the direct impacts on air pollution by the BOEM-planned offshore wind farms are expected to be negligible.

Control of Large Wind Energy Converters for Aeroacoustic Noise Mitigation with Minimal Power Reduction

The population is often opposed to wind turbines being erected near their homes, mainly because the machines are noisy, especially at night. In an effort to establish a compromise between the needs of the people and the fulfilment of energy demands, wind turbines have the ability to switch between day and night operation by reducing the rotation speed during the night, resulting in a loss of generated power. The present study investigates simple models for noise emission, propagation, and prediction, with the objective of proposing a control system configuration that continuously adjusts the rotational speed as much as necessary until it matches sound level regulations while minimising power losses. Thus, several approaches are implemented and tested with a very large reference wind turbine. The simulation results of a reference wind turbine show that the approaches provide significant improvements in sound reduction as well as in power conversion.

A meso–micro atmospheric perturbation model for wind farm blockage

As wind farms continue to grow in size, mesoscale effects such as blockage and gravity waves become increasingly important. Allaerts & Meyers (J. Fluid Mech., vol. 862, 2019, pp. 990–1028) proposed an atmospheric perturbation model (APM) that can simulate the interaction of wind farms and the atmospheric boundary layer while keeping computational costs low. The model resolves the mesoscale flow, and couples to a wake model to estimate the turbine inflow velocities at the microscale. This study presents a new way of coupling the mesoscale APM to a wake model, based on matching the velocity between the models throughout the farm. This method performs well, but requires good estimates of the turbine-level velocity fields by the wake model. Additionally, we investigate the mesoscale effects of a large wind farm, and find that aside from the turbine forces and increased turbulence levels, the dispersive stresses due to subgrid flow heterogeneity also play an important role at the entrance of the farm, and contribute to the global blockage effect. By using the wake model coupling, we can explicitly incorporate these stresses in the model. The resulting APM is validated using 27 prior large-eddy simulations of a large wind farm under different atmospheric conditions. The APM and large-eddy simulation results are compared on both mesoscale and turbine scale, and on turbine power output. The APM captures the overall effects that gravity waves have on wind farm power production, and significantly outperforms standard wake models.

Experimental study and finite element analysis on shear performance of offshore wind turbine jacket foundation

The repeated extrusion of sea ice leads to the continuous collapse of marine structures. In this paper, the shear test and finite element simulation analysis of jacket foundation of large offshore wind turbine are carried out. The results show that the bearing capacity of the structure under positive displacement loading is slightly larger than that under negative displacement loading, and the bearing capacity is increased by 5.3 %. The lateral support joints have compression bending and fracture failure modes, and the maximum strain is 3600 με. Finally, the failure mode, energy dissipation capacity and bearing capacity of jacket foundation under reciprocating displacement load are obtained. At the same time, the ice load action model is established, and the research results provide experimental basis for improving the shear performance of offshore wind turbine.

Equatorial Dynamics Significantly Weakened the Southward Somali Current Circulation during 1997–98

Abstract Based on reanalysis data and numerical model, this study documented an unusually weak southward Somali Current (SSC) circulation, an anticlockwise circulation in the western Arabian Sea, during the winter of 1997 to spring 1998, throughout the two-decade period from 1994 to 2017. Theoretical analysis and model simulations suggest that the exceptionally weak SSC circulation was primarily caused by abnormal wind forcing around the equator, associated with extreme climate events. The strong equatorial easterly anomalies are mainly responsible for the weakening of the SSC circulation north of ∼6°N and contribute to about half of the weakening south of ∼6°N, by regulating the layer thickness along the eastern boundary, following the time-dependent Sverdrup relation. The large negative wind stress curl anomalies near the equator contribute to another half of the weakening south of ∼6°N. This research underscores the dynamic connection between the circulation in the western basin and equatorial dynamics during extreme climate modes. Significance Statement The dynamic linkage between the western basin circulation and the equatorial forcing remains elusive. Based on reanalysis data, theoretical analysis, and numerical simulations, this study has confirmed that the exceptionally weak southward Somali Current circulation during the winter of 1997 to spring 1998 was significantly influenced by abnormal wind patterns around the equator, triggered by extreme climate modes.

Boosting the Development and Management of Wind Energy: Self-Organizing Map Neural Networks for Clustering Wind Power Outputs

Aimed at the information loss problem of using discrete indicators in wind power output characteristics analysis, a self-organizing map neural network-based clustering method is proposed in this study. By identifying the appropriate representativeness and topological structure of the competition layer, cluster analysis of the wind power output process in four seasons is realized. The output characteristics are evaluated through multiple evaluation indicators. Taking the wind power output of the Hunan power grid as a case study, the results underscore that the 1 × 3-dimensional competition layer structure had the highest representativeness (72.9%), and the wind power output processes of each season were divided into three categories, with a robust and stable topology structure. Summer and winter were the most representative seasons. Summer had strong volatility and small wind power outputs, which required the utilization of other power sources to balance power supply and load demand. Winter featured low volatility and large wind power outputs, necessitating cooperation with peak-shaving power sources to enhance the power grid’s absorbability to wind power. The seasonal clustering analysis of wind power outputs will be helpful to analyze the seasonality of wind power outputs and can provide scientific and technical support for guiding the power grid’s operation and management.

A smart multiphysics approach for wind turbines design in industry 5.0

This paper aims to develop a smart multiphysics approach for wind turbine design utilizing Industry 5.0. A new blade profile is developed and optimized by non-dominated sorting genetic algorithm II (NSGA-II) for shape design, and a 3D modeling of wind turbines is proposed. The aerodynamic modeling of a horizontal axis wind turbine (HAWT) is an important step in the design of wind turbines. The blade geometry design plays an important role in a wind turbine to maximize the aerodynamic performance and extract as much kinetic energy as possible from the wind resource. This paper addresses a high-level design and optimization for the parameters of a new blade. Moreover, a 3D modeling of large wind turbines (>7 MW) is proposed that can be used in wind farms. This approach can be used in real-time design in Industry 5.0 using different data from sensors. Finally, the optimized blade increases the produced power by 10% (from 7.5 MW to 8.2 MW). The proposed approach allows people to work alongside machinery to improve processes and provide personalization for companies manufacturing wind turbines.

A multi-task spatio-temporal fusion network for offshore wind power ramp events forecasting

With the accelerated development of offshore wind farms, the impact of Wind Power Ramp Events (WPRE) on power systems has become more pronounced. Accurate prediction of WPRE is essential for mitigating their adverse effects on the grid. However, current studies on offshore WPRE are limited, especially regarding their spatio-temporal correlations and variability across large wind farm clusters. To address this, a novel forecasting approach using a multi-task spatio-temporal fusion network is presented. This method improves WPRE prediction by integrating spatial and temporal data and utilizing multitask learning to characterize ramp features. Firstly, an index system for WPRE characterization is developed, including metrics such as power change rate and duration. Based on this, an X-means classification method for WPRE is established. Secondly, a spatio-temporal encoder combining graph convolutional networks and temporal attention mechanisms has been proposed to model the spatiotemporal dependencies of meteorological changes in offshore wind farms. Finally, a multitask learning framework has been introduced to predict WPRE characteristic indices and occurrence probabilities. This approach enhances feature extraction efficiency through the joint feedback of multiple related tasks, thereby improving prediction accuracy. A case study using data from five offshore wind farms in Eastern China validates the efficacy of the proposed method.

Correction: Wang et al. Studies on the Experimental Measurement of the Low-Frequency Aerodynamic Noise of Large Wind Turbines. Energies 2024, 17, 1609

In the original publication [...]

Variation of Heliostat Wind Loads in a Radial Field Array Model

The design of heliostats throughout a concentrated solar power (CSP) plant are currently of uniform design, however, research into the variation in heliostat wind loading within a radially staggered field array can provide insight into potential material cost savings in heliostat field design. Experimental investigation was carried out on a field array model in the University of Adelaide large wind tunnel. A total of 64 heliostat models of size 0.1 × 0.1 m were radially arranged over four surround rows. Four 3-axis load cells were utilized to analyze field array cases of morning (0700 hours), noon (1200 hours), and evening (1700 hours) configurations. Angle calculations for beam reflection were made for the 21st March (equinox). Results show mean and peak drag force coefficients reduce with distance into the field for a 1700 hour case due to high upstream blockage. Comparing the downstream drag and lift force coefficients against each test configuration, when heliostats are at steep elevation angles, high flow blockage leads to a reduction of coefficients downstream (0700 hour and 1700 hour cases). When elevation angles are reduced (1200 hours), drag and lift coefficients increase with distance from the central tower in the downstream direction. Results also show the central tower increases load coefficients on downstream heliostats, and therefore should be considered in heliostat wind loading and field design.

Analysis of Wind Power Fluctuation in Wind Turbine Wakes Using Scale-Adaptive Large Eddy Simulation

In large wind farms, the interaction of atmospheric turbulence and wind turbine wakes leads to complex vortex dynamics and energy dissipation, resulting in reduced wind velocity and subsequent loss of wind power. This study investigates the influence of vortex stretching on wind power fluctuations within wind turbine wakes using scale-adaptive large eddy simulation. The proper orthogonal decomposition method was employed to extract the most energetic contributions to the wind power spectra. Vertical profiles of mean wind speed, Reynolds stresses, and dispersive stresses were analyzed to assess energy dissipation rates. Our simulation results showed excellent agreement when compared with wind tunnel data and more advanced numerical models, such as the actuator line model and the actuator line model with hub and tower effects. This highlights the important role of coherent and energetic flow components in the spectral behavior of wind farms. The findings indicate a persistent energy cascading length scale in the wake of wind turbines, emphasizing the vertical transport of energy to turbine blades. These results complement existing literature and provide new insights into the dynamics of wind turbine wakes and their impact on wind farm performance.

Actuators for Large Wind Energy Systems—A Tutorial-Focused Survey

Undoubtedly, wind turbines are currently one of the most significant contributors to clean energy. Therefore, it is crucial to enhance the capability of wind turbines, which in turn leads to an increase in their dimensions. Nevertheless, only advanced control systems can guarantee the optimal and secure operation of these huge machines. On the other hand, the precise control of these modern wind turbines is only achievable through the use of highly specialised actuators. Despite their importance, actuators have historically been overlooked and seen as secondary components in control systems. However, in modern machines, actuators are required to manipulate multiple tonnes or manage thousands of volts and amperes within short times. Consequently, greater emphasis must be placed on their handling and operation. This study aims to review actuators for modern large wind energy converters from a control engineering perspective, using a tutorial approach.

Integrating Learning-Driven Model Behavior and Data Representation for Enhanced Remaining Useful Life Prediction in Rotating Machinery

The increasing complexity of modern mechanical systems, especially rotating machinery, demands effective condition monitoring techniques, particularly deep learning, to predict potential failures in a timely manner and enable preventative maintenance strategies. Health monitoring data analysis, a widely used approach, faces challenges due to data randomness and interpretation difficulties, highlighting the importance of robust data quality analysis for reliable monitoring. This paper presents a two-part approach to address these challenges. The first part focuses on comprehensive data preprocessing using only feature scaling and selection via random forest (RF) algorithm, streamlining the process by minimizing human intervention while managing data complexity. The second part introduces a Recurrent Expansion Network (RexNet) composed of multiple layers built on recursive expansion theories from multi-model deep learning. Unlike traditional Rex architectures, this unified framework allows fine tuning of RexNet hyperparameters, simplifying their application. By combining data quality analysis with RexNet, this methodology explores multi-model behaviors and deeper interactions between dependent (e.g., health and condition indicators) and independent variables (e.g., Remaining Useful Life (RUL)), offering richer insights than conventional methods. Both RF and RexNet undergo hyperparameter optimization using Bayesian methods under variability reduction (i.e., standard deviation) of residuals, allowing the algorithms to reach optimal solutions and enabling fair comparisons with state-of-the-art approaches. Applied to high-speed bearings using a large wind turbine dataset, this approach achieves a coefficient of determination of 0.9504, enhancing RUL prediction. This allows for more precise maintenance scheduling from imperfect predictions, reducing downtime and operational costs while improving system reliability under varying conditions.

Control of Large Wind Energy Systems Throughout the Shutdown Process

This contribution examines the control problem for very large wind energy converters during shutdown operation and analyses the most important control approaches. The control methods make use of the built-in conventional control infrastructure, but control system reconfigurations are undertaken in order to meet the demands of the shutdown control operation. Hence, the torque controller as well as the collective pitch controller (CPC) are redesigned from their regulator functions to reference tracking control systems with constraints. In addition, the CPC is combined with a feedforward controller in order to gain responsiveness. Constraints in magnitude and rate are managed by a modified anti-windup mechanism. Simulations of a 20 MW reference wind turbine verify the performance of the approaches.

Three-dimensional spatiotemporal wind field reconstruction based on LiDAR and multi-scale PINN

In this numerical study, we use a multi-scale version of physics-informed neural network (PINN) for wind field reconstruction from LiDAR measurements. The reference velocity field is obtained from high-fidelity large-eddy simulation of neutral atmospheric boundary layer, and the LiDAR measurement strategy is restricted to that of a real LiDAR. The multi-scale PINN reconstructs the velocity field by minimizing a combination of the L2-error with respect to the LiDAR measurements and residuals of the governing equations. Our most significant finding is that adding a multi-scale layer to PINN enables us to: (i) capture wider range of scales, (ii) reconstruct the flow field outside the LiDAR scanning area, and (iii) reach faster convergence. We also find that for volumetric flow reconstruction and to deal with uncertainty in the wind direction, the reconstruction accuracy can be greatly improved by employing multiple LiDAR devices. Overall, our results show that the normalized errors in the reconstructed wind field are lower than 5% for all the experiments conducted in this study and that the errors do not increase even in the presence of measurement noise levels of typical LiDAR. These findings show the feasibility of three-dimensional dynamic wind field reconstruction across large wind farms using LiDAR devices.

Analysis and suppression research of high lift device noise based on large aeroacoustic wind tunnel test

The exterior noise of large aircrafts is an important aspect for the airworthiness certification. The high-lift device is one of the key noise sources for large aircrafts, thus the analysis and reduction of high-lift device noise is important and meaningful. Based on the 5.5m×4.0m Aeroacoustic Wind Tunnel and a half model for the aeroacoustic study of large scale aircrafts, the high-lift device noise properties were studied experimentally. First, the experimental platform, the test model, the test system and noise reduction design were introduced. Then, based on the experimental results, the noise characteristics of the high-lift devices were analyzed. The effects on the noise from the angle of attack and the wind speed were compared. The effects on the high-lift device noise from the noise reduction design on slat and flap were investigated. The results show that, the high-lift device noise has a broad frequency spectrum, with some peak frequency components. As the angle of attack is increased, the noise of the model is generally increased in the medium to high frequency range. With the wind speed increased, the noise is increased dramatically, and the frequency spectra keep a good similarity. The peak noise is suppressed by the noise reduction design.

Simplified Polynomial-asymptotic-distribution Actuator Disc (SPAD) method for wind turbine wake simulation based on k-[formula omitted]-[formula omitted] turbulence model

In large wind farms, wake effect has a negative impact on the annual production of wind turbines. As a result, wind turbine wake simulation is important for wind farm micro-sitting to minimize power loss and maximize energy production. Coupled with Reynolds Averaged Navier–Stokes turbulence model, Simplified Uniform-distribution Actuator Disc method is the most widely-used rotor model for wake simulation in engineering practice. The conventional simplified uniform-distribution actuator disc models suffer discrepancies of wake velocity prediction compared with the measurements in the near wake region. The novelty of the current research is to propose a Simplified Polynomial-asymptotic-distribution Actuator Disc method based on k−ϵ−fP turbulence model for cylindrical actuator disc cell region with finite thickness, which is simple for implementation and improves the accuracy of wake simulation compared with conventional method. The proposed method conforms to momentum conservation and is as simple as conventional method because of its polynomial formulation and no requirement for aerodynamics data in Generalized Actuator Disc model. From the verification and validation cases, it can be found that the proposed model has better agreement with both high-fidelity actuator line model and the field test measurements from Lillgrund wind farm compared with conventional model. The proposed actuator disc model has the potential to be applied to the micrositting of large offshore wind farms in the future.

A Numerical Investigation of the Influence of the Wake for Mixed Layout Wind Turbines in Wind Farms Using FLORIS

A common retrofitting method for wind farms is the replacement of low-power turbines with high-power ones. The determination of the optimal replacement sequence for the purpose of maximizing revenue is a significant challenge. This paper employs a combination of FLORIS and a sequencing algorithm to simulate the power output resulting from the replacement of 1.5 MW small turbines with 5 MW large turbines. This study demonstrates that the optimal strategy for maximizing the overall power output is to replace the turbines in the first column. When the turbines situated in the first column have already undergone replacement or are unable to be replaced due to the characteristics of the terrain, it would be prudent to prioritize those in the final column. In the case of staggered arrangements, priority should be given to diagonal points that do not have turbines situated behind them. In the case of replacing the same number of large wind turbines, the preferred replacement option has a minimal impact on the power output of the existing small wind turbines, with an estimated reduction of 0.67%. This effectively enhances the economic efficiency of wind farm renovation.

Enhancing wind turbine load response tool through geometric non-linearities correction: Case study on DEFlex and design load comparison

This paper introduces a rapid correction technique applied in DEFlex, Ørsted’s in-house aero-servo-hydro-elastic solver for offshore wind turbines. The technique addresses geometric non-linearities from large wind turbine blade deflections, focusing specifically on correcting the angle of attack for torsional deflections. To assess the implementation, comparisons were made with the literature and HAWC2, using the IEA 15-MW wind turbine blade subjected to static and simplified dynamic loading. Additionally, simulations of normal operation scenarios, both with and without turbulence, were performed on a real wind turbine exceeding 10 MW rated power. The results were compared with Siemens Gamesa Renewable Energys tool BHawC, which utilizes a co-rotational formulation for capturing large blade deflections. The findings reveal that coupling DEFlex with the correction method accurately captures the angle of attack dynamics and significantly improves the agreement of fatigue loads.