Wind Turbine Farm Research Articles

Preface

The eighth Wake Conference, held between 20th and 22nd of June 2023, marks nearly a decade and a half of successful international conferences focused on advancements in the field of wake dynamics. This bi-annual conference, held for the past decade on the island of Gotland, Sweden seeks to aid in this by providing a venue for scientists and PhD students working in the field of wake dynamics to come together and share their research and expertise.To understand the need for this conference one only needs to look to modern wind farms. These massive energy facilities rival the production of a nuclear power plant and consist of dozens or potentially hundreds of individual wind turbines. The numbers of turbines involved make it unavoidable that they will invariably be impacted by the wakes of upstream turbines. This wake interaction results in a decreased power production, caused by the lower kinetic energy in the wind, and an increase in the turbulence intensity. This decrease in power emphases the importance of understanding the physical nature of vortices and their dynamics in the wake of a turbine when designing the layout of these immense wind farms. The clear importance of farm design, and the related interest in the field of wake and wind farm aerodynamics, can be seen in the growing number of scientific articles on the subject.Further, in order to make a substantial impact on one of the most significant challenges of our time - global climate change - the wind industry’s growth must continue. A part of making this growth possible, and to solve some of the scientific problems of doing so, will require research into the different areas of the physics of wind turbine and wind farm wakes. To this end, the conference covers the following subject areas: Wake and vortex dynamics, instabilities in trailing vortices and wakes, simulation and measurements of wakes, analytical approaches for modeling wakes, wake interaction along with other wind farm investigations.We would like to thank all those that assisted in the review process for the 55 papers which constitute the proceedings of the Wake Conference 2023 that were selected to be published. We are also thankful for the help we have received from the editorial team at IOP Publishing during the process.Visby, Sweden June 2023 The Organizers, Professor Stefan Ivanell, Uppsala University Professor Jens Nørkær Sørensen, Technical University of Denmark List of Editorial Committee are available in the pdf.

Marine birds: Vision-based wind turbine collision mitigation

Throughout their evolution seabirds have not had to contend with the collision risk posed by discrete objects that extend into their flight space above the water surface. However, the recent introduction of offshore wind turbines has significantly increased the potential for collisions. Bird collision risk with Offshore Wind Farm (OWF) turbines is now a major consenting consideration for OWF projects due to potential local population impacts on birds, especially those associated with protected sites. Therefore, the possibility of reducing those risks through a simple mitigation is highly desirable. Key elements for the design of vision-based mitigation measures aimed at reducing the collision of marine birds with wind turbines should be based upon knowledge of the vision of birds, not the vision of humans. May et al. (2020) tested a vision-based wind turbine mitigation measure and reported a modelled 70% reduction in annual turbine-blade collision mortality rate at a terrestrial location in a suite of 19 bird species. The aim of the present proposals is to extend this vision-based mitigation approach and increase its applicability to a broad suite of bird species considered vulnerable to collisions with wind turbines at sea. Key aspects of the vision, behaviour and ecology of marine birds which contribute to their collision risk under a range of natural viewing conditions are reviewed. The same information is then employed to give insights into the requirements of vision-based mitigation measures. We argue that the internal visual contrast of wind turbines should be increased using achromatic patterns applied to blades and pylons. These patterns should reduce the collision vulnerability of marine birds in general and should be effective under a range of visibility conditions determined by natural light levels and weather conditions. The measures should allow birds with different flight speeds and visual acuities to detect turbines sufficiently early to allow alteration of flight direction and avoid collision. The proposed mitigation requires changes to the appearance of wind turbines that can be implemented at the time of manufacture. They do not interfere with statutory requirements already required for the marking of turbines for the benefit of shipping and aircraft.

A new Gaussian analytical wake model validated by wind tunnel experiment and LiDAR field measurements under different turbulent flow

Wind turbine wake has a great effect on wind power generation and fatigue loads of downstream wind turbines in wind farms. Analytical engineering wake model is adopted to evaluate the power loss caused by turbine wake for the optimization of wind farm layout. Gaussian distribution is often employed in wake model. Traditional Gaussian distributions, on the other hand, are unable to accurately describe wind speed especially near the wake boundary. Besides, the variable turbulent environment in wind farms with complex terrain poses a challenge to the turbine wake model. In this paper, the unfitness of the Gaussian wake model especially near the wake boundary is addressed and a Gaussian wake model with new turbulence intensity model is proposed. Compared with previous models, this new wake model has a more suitable wind speed distribution. The new wake model is then validated using wind tunnel measurements under different turbulence intensity. Results indicate that the new Gaussian wake model can more correctly predict the wind turbine wake than the previous Gaussian wake models. The new Gaussian wake model suitable shows a good application prospect in wind farm layout optimization which improves the power generation and reduces the power generation costs.

Potential biological impacts of very low frequency acoustical energy produced by offshore wind turbine energy generation

Offshore wind turbine farms are being planned and installed around the US Outer Continental Shelf. These will be introducing a panoply of noises into the environment, from siting, through installation, to operations, and eventually decommissioning. Noise concerns from these activities are typically framed within frequency bands of concern defined by governmental regulatory agencies. A less considered noise is the very low frequency thump generated by the motion of the turbine blades as they intersect the “stagnate wind area” on the windward side of the mast. This “infrasonic” thump contains a lot of energy and is in the perceptual range of migratory birds and baleen whales. Given that whales and birds use “infrasonic” sound and barometric pressure signals for navigation and migration cues, the installation of thousands of turbines off the Atlantic Outer Continental Shelf, and hundreds off the Pacific OCS, may impose significant impacts on migratory passerine birds along the “Atlantic Flyway,” Procellariiformes on both the Atlantic and Pacific OCS, and all baleen whales within the infrasonic energy propagation reach of the turbines.

Multi‐parameter optimization of performance and economic viability of Ferris wheel wind turbine for low wind speed regions in Africa

AbstractPrevious studies on wind turbine and wind farm optimization for Levellized cost of energy (LCOE) and annual energy production (AEP) have focused on horizontal axis wind turbines (HAWT) and vertical axis wind turbines (VAWT). Regions with lower wind speed resources tend to have a higher levellized cost of energy and lower annual energy production. In this paper, the authors investigate the optimization of a novel, Ferris wheel wind turbine (FWT) for low wind speed regions of Africa. The research used an Excel‐based Multi‐Objective Optimization (EMOO) model. The EMOO program has both binary‐coded and real‐coded Elitist Non‐Dominated Sorting Genetic Algorithm (NSGA‐II). The optimization is conducted by studying the effect of varying the rim diameter, number of blades, and the rated wind speeds for an 800‐kW generator on the performance and economics in 21 African cities. The results show that, on average, the return‐on‐investment increases over the base design by up to 182%, and both the simple payback period (SPP) and the levellized cost of electricity decreased by 39% as the rim diameter increases combined with a 50% reduction in blade numbers. In addition, a 75% reduction in blade numbers caused a further 32% decrease on average for both the simple payback period and the levellized cost of electricity.

Flight Type and Seasonal Movements Are Important Predictors for Avian Collisions in Wind Farms

Wind farms are an alternative energy source mitigating environmental pollution. However, they can have adverse effects, causing an increase in mortality for wildlife through collision with wind turbines. The aim of this study was to investigate the risks of bird collisions with wind turbines linked to species-specific variables. For this purpose, we have analysed the dead birds involved in wind farm collisions that were admitted to two rescue centres in Spain over a period of 16 years (2001–2016; full dataset: n = 3130). All the birds analysed in this study were killed by turbines in wind farms. We performed two linear models using all species and a reduced dataset (bird of prey and passerine having more than four collisions) that included group, seasonal movements, flight type, length, and the number of pairs for the Spanish and European populations. The coefficients and the percent of variance explained by each relevant variable were determined in the models and the real values were compared with predicted values to visualise the goodness of fit. We found that the flight type was the most important variable explaining 35% of the total variability for the model including all species and 29% for the reduced dataset respectively, followed by seasonal movement type (4%/17% respectively) and the Spanish population (4%/6%). Subsequent analyses suggested that species with hovering, song-flights and active soaring flights are more susceptible to collisions with wind farms, and that species showing partial migration have a significant peak of collisions across spring and autumn. The estimated species-specific collision index can help in modelling the theoretical risk of collision with wind turbines, depending on the species existing in the area and their predicted values of vulnerability, which is linked to flight types and seasonal movements.

Optimized Active Power Dispatching of Wind Farms Considering Data-Driven Fatigue Load Suppression

The active power fluctuation of wind turbines is not only related to their friendliness to the grid, but also to their fatigue damage. In this paper, the active power of wind turbines in wind farms is optimally scheduled to achieve the suppression of fatigue load of wind turbines. Considering the complexity of fatigue load calculation, it is difficult to apply to real-time active scheduling using metrics that directly characterize fatigue load. To address this problem, a data-driven modeling method for wind turbine fatigue based on deep neural network (DNN) is proposed in this paper, and the relationship between wind speed, power and other easily measurable parameters and fatigue load is established. Further, an improved multi-objective grey wolf optimizer (MOGWO) is designed to achieve the wind farm active scheduling process with the data-driven fatigue calculation results as the optimization objective. The results show that: The fatigue load prediction model of data-driven fatigue calculation proposed in this paper has a satisfactory effect, and the fatigue load of wind turbines can be effectively reduced by adjusting the active power.

GWTSP: A multi-state prediction method for short-term wind turbines based on GAT and GL

Accurately predicting the state of wind turbines in wind farms helps to improve productivity, perform preventive maintenance, and reduce operation and maintenance costs. Deep learning can handle complex data and automatic feature extraction in prediction tasks with high accuracy, scalability, and generalization capabilities. However, most current research focuses on predicting wind turbines' joint or individual states, which needs to be improved for real-world wind farms. Therefore, we model the time series of wind turbine states, design a graph learning (GL) module to extract model features further and propose a GWTSP (GAT-based Wind Turbine State Prediction) to predict the multivariate states of wind turbines. Experiments show that our model achieves better prediction accuracy, and the experimental results are better than the baseline model.

Can wind turbine farms increase settlement of particulate matters during dust events?

Dust event is one of the main environmental problems affecting several countries of the world, and the dust burden is a severe threat to human health, environment, and socio-economic activities. Nevertheless, controlling windblown dust is a major challenge that hitherto has not been effectively overcome. Wind turbines can extract a considerable amount of kinetic energy from wind, reduce wind speed, and increase turbulence in the following wake, but can wind turbine farms increase the settlement of particulate matter during dust events? To examine this question, this study develops a framework based on FLOw Redirection and Induction in Steady State that calculates essential parts of the flow and dust concentration fields in wind farms. Eight different layouts and three wind turbines have been simulated to find the best wind farm arrangement that yields maximum dust deposition. It is found that wind farms, depending on the layout, decrease the wind speed by 8%–14.5%. In the best scenario, wind farms increase PM2.5 deposition by 23.3% and PM10 deposition by 34.2% compared to no wind farm case. Therefore, this study suggests that using wind turbine farms as wind barriers, provided the surface itself is not erodible, can be an effective way to control windblown dust.

Black-tailed gulls alter their flight height and airspeed according to wind conditions during their coastal commuting trips

Seabirds are expected to increase their flight height in tailwind and to increase their airspeed in headwind during goal-oriented flight to minimize their cost of transport. To understand how flapping birds respond to variability in wind speed and direction experienced during their commuting flights between their breeding colony and foraging areas, we measured the flight height and speed of black-tailed gullsLarus crassirostrisusing GPS loggers. We analyzed the relationships between these flight parameters and local wind speed and direction. Over the course of the 2016 to 2018 breeding seasons, we tagged 105 birds at 2 colonies in northern Hokkaido, Japan. A total of 90 flight track-lines within a 500 m radius of 5 coastal meteorological stations were analyzed. The median flight height ranged from 0 to 153.8 m, and the median ground speed and airspeed were between 18.6-82.1 and 19.5-93.0 km h-1, respectively. Gulls flew higher with greater tailwind speed, supporting the hypothesis that birds utilize greater wind assistance at higher altitudes. Furthermore, gulls increased their airspeed under strong headwind conditions, suggesting they adjust airspeed to achieve the most cost-effective speed to reach their destination. Better understanding the drivers of seabird flight height is key to assessing the potential for collisions with wind turbines in coastal and offshore wind farms. These findings provide useful information to reduce collisions with coastal and offshore wind facilities.

Control Strategy and Economic Benefit Analysis of Wind Turbine Blade Replacement

This paper takes a 1.5 MW wind turbine wind farm in Jilin province as an example. It analyzes the improvement of blade lengthening in wind turbine power generation by optimizing control strategy and adopting intelligent control technology. And it compares the economic benefits of replacing 82 blades and 89 blades and provides a theoretical and practical basis for blade lengthening transformation of other wind farms.

Hybrid Pointer Based Blockchain Solution and Ledger Data Services Interoperability

The Blockchain concept has erupted from decentralized digital currency world and has reached a high penetration in other areas where the certification of transactions and its immutability is required. The experience on elevated security to event time driven data assertion was transferred from the digital currency to the data driven enterprise world. In applications were highly regulated and certified data intensive systems are needed, Blockchain becomes a strong solution. Areas like the management of the lifecycle of reusable rockets, aeronautics, aerial surveillance, aviation safety, wind turbine and solar farms, electric grids and others, the amount of generated data to be registered and certified is considerable. Many implemented Blockchain solutions tend to register and add to the Blockchain, large amounts of data that reduce the Blockchain management and usability. This work proposes a Hybrid Pointer Based Blockchain solution to overcome the issues, by integrating several parallel services that work side by side with distributed, secured and certified peer node systems that guarantee the data immutability and interconnection to the different blockchains that interact with each other. The proposed Blockchain solution presented in this work interacts with the peer nodes edge services, from software services to hardware generated data and AI assessments.

Study of Vertical-Axis Wind Farm Layouts Using a 2D Actuator-Cylinder RANS Model

The actuator-cylinder RANS (Reynolds-averaged Navier-Stokes equations) model was used to study the performance of two different arrays of wind turbines. The staggered array proved to be more efficient than the fish-school array (grouped in pairs) in most directions; however, the fish-school array outperformed the conventional staggered array when the pairs of turbines were facing the wind. Increases in global power coefficient up to 16% were found when the wind speed was 8 ms-1 and up to 10% when the wind speed was 10 ms-1. Despite the fish-school array being slightly less efficient, this array yielded almost twice as much power density as the staggered array in almost all directions. The current methodology proves to be a fast tool for the estimation of vertical-axis wind turbine farms compared to full RANS simulations.

Wind inflow observation from load harmonics via neural networks: A simulation and field study

In this work, feed-forward neural networks are used to estimate the inflow of a wind turbine in real time during operation. A previously published method achieved this capability by correlating the turbine response to the wind inflow via a piecewise-linear model. Specifically, the existing formulation used the once-per-revolution harmonics of both in- and out-of-plane blade bending moments to estimate four wind parameters: the vertical and horizontal shears, and the vertical and horizontal misalignment angles. The novel method presented here builds on the same concept, but replaces the linear model with neural networks. Compared to the previous approach, the neural-based one is simpler to tune and use, because it decouples the wind parameters, which can be observed each one independently from the others. The proposed formulation is tested using both aeroservoelastic simulations and field data obtained on a 3.5 MW machine. In the field, independent measurements of the inflow are provided by a nearby hub-tall met mast, providing data for the training and verification of the wind observer. Notwithstanding some limitations of the experimental setup, results indicate that the neural networks produce estimates of the shears and yaw misalignment angle with accuracies compatible with typical use cases in wind turbine and wind farm control. Additionally, the neural-based observer appears to slightly outperform the original formulation in the lower wind speed range, where stronger nonlinearities in the load-wind mapping can occur.

Optimal design and operation of a multi-energy microgrid using mixed-integer nonlinear programming: Impact of carbon cap and trade system and taxing on equipment selections

Multi-energy microgrids comprise various energy sources such as solar, wind, hydro, biomass, oil, gas, and coal. Optimal configuration and scheduling of multi-energy microgrids enhance energy efficiency and reduce carbon dioxide emissions. In this work, a novel and detailed mixed-integer nonlinear programming (MINLP) model is proposed to compare the effects of carbon emission taxing and cap and trade system on the optimal equipment selection and scheduling under the regulatory effects of the Paris Agreement. The candidate equipment to be selected by the MINLP model includes two solar panel arrays, two wind turbine farms, two integrated gasification combined cycles, two combined heat and power units, two conventional generators, one biogenerator, one battery, two bio-gasifiers, and one power-to-gas system. The impact of carbon dioxide price, taken as 50, 75, and 100 $/ton, is investigated for both emission taxing and carbon dioxide cap and trade system. Equipment selections with the multi-period seasonal data and the carbon dioxide prices of 50 $/ton and 75 $/ton in stand-alone mode are the same for both cases without selecting renewable sourced generators. For the same conditions and with a carbon dioxide price of 100 $/ton, the MINLP model selects one wind turbine farm, one biogenerator, and one bio-gasifier. Moreover, the power-to-gas system is chosen in electricity grid-connected mode under emission taxing with the carbon dioxide price of 100 $/ton. For both emission taxing and cap and trade system, the trials using the seasonal data and the yearly average data with carbon dioxide price of 75 $/ton in stand-alone mode contributes to different equipment selections. Results show that carbon dioxide price may be a crucial parameter for equipment selection and must be taken into account in the design phase for optimal design and management. Finally, the MINLP solvers SCIP, BARON, and DICOPT, are compared in terms of CPU times and equipment selections.

Large Eddy Simulation of wind turbine wake interaction in directionally sheared inflows

A coupled Large Eddy Simulation (LES) and turbine aeroelastic code was used to investigate the impact of directionally sheared inflows on wake interaction in a four-inline turbine array. The wake characteristics in large directional shear inflows were studied to quantify their influence on fatigue loads on the downstream turbines. Coriolis forces were varied by setting the latitude to 0°, 45° and −45°, to produce zero directional shear, the wind veering (Northern hemisphere) and wind backing (Southern hemisphere), respectively, where the hub-height mean wind speed for all cases was controlled to 7 m/s. This was intended to evaluate wind farm turbine operations in the Southern hemisphere which have not been studied before. The results showed that the wake rotations are affected by the lateral velocity of the ambient wind, which causes differences in radial wake expansion and wakes shape twist angle in the Northern and Southern hemispheres. The symmetric wakes in the zero directional shear case led to lower fatigue loads on the blades and rotor shaft compared to skewed wakes. The flapwise blade-root bending moment of downstream turbines at 1P frequency under wind veering (Northern hemisphere) was found to be approximately three times that under wind backing (Southern hemisphere).

Research on neural network wind speed prediction model based on improved sparrow algorithm optimization

The wind speed signal measured by the mechanical anemometer lags behind the wind speed at the wind wheel surface of the wind turbine in time. The wind speed reconstruction algorithm adopted by the ordinary lidar anemometer is insufficient. For the sake of enhancing the accuracy of wind speed forecast on the surface of wind turbines in wind farms for wind turbine pitch control strategies, an modified sparrow search algorithm (SSA) based on sinusoidal chaotic mapping is proposed to improve the BP neural network wind speed prediction model. According to the characteristics that lidar measure wind speed at multiple points, a multiple regression forecast model is build, and then the wind speed is predicted. Simulation tests were conducted using lidar wind measurement data from a wind farm in Xinjiang and analyzed and in contrast to a BP neural network wind speed prediction model. The experimental results indicated that the four error evaluation indicators of the Sine-SSA-BP algorithm are all smaller than the BP neural network. In contrast to the single BP neural network wind speed forecast model, sine chaotic mapping modified sparrow search algorithm optimized BP neural network has significantly improved prediction accuracy.

Numerical Investigation on Vortex-Induced Vibration over Circular Cylinder to be used for Energy Harvesting Application

Renewable energy is energy generated through inexhaustible processing resources, like sunlight, wind, etc. Existing and popular wind energy harvesting technology is wind turbine farms that have very high installation costs and demand strong wind flow to deliver sufficient power. Thus, wind energy generation based on the vortex-induced vibration-based energy harvester has become a more popular field of interest for many researchers. The vortex shedding produced by the unsteady incompressible flow over a bluff body has been used to excite the piezoelectric plate, generating electricity by its vibration. Hence generating an optimised vortex shedding and obtaining the optimised oscillation frequency. This paper investigates the computational fluid dynamics-based flow analysis over a smooth cylinder carrying a plate. The Reynolds number, Re, is 100 used for unsteady incompressible flow and the plate density ratio is varied from 5 – 40. The variation in lift and drag forces and frequency of vibration, and maximum vibration amplitude achieved by the piezoelectric plate is observed and determined. The results presented here help design a better and more efficient vortex-induced energy harvester.

Windfarm collisions in medium‐sized raptors: even increasing populations can suffer strong demographic impacts

AbstractThe impact of bird mortality by collision on windfarms has often been evaluated at the individual level, but rarely at the population level. The Lesser kestrel Falco naumanni is an endangered short‐lived migratory raptor, susceptible to collision with wind turbines. We evaluated the impacts of windfarm turbine collisions on the demography of the largest lesser kestrel population in France. Using data from local monitoring of reproduction and windfarm mortality surveys, combined with capture‐recapture ringing data at a nearby population, we quantified vital parameters of fecundity and survival in order to parameterize a matrix population model to study the viability of this population. The breeding success was high and varied in synchrony with survival probabilities. Between 2013 and 2020, 43 carcasses were found below wind turbines, and when accounting for carcass detection and persistence rates, the true mortality should approach 154 individuals in that period, i.e. 3% of the studied population was affected by collisions each year. The matrix model showed that the population growth observed was only possible if there was a constant recruitment of 26 immigrant individuals each year into the population. Without the excess mortality by the windfarm, we predict that this population would have 22% more breeding pairs than what was observed in 2020. Simulations over 30 years showed that, under the current immigration rate, the population should decline if the excess mortality exceeds 11%. If immigration ceases, the population would decline above 5% excess mortality per year. It is urgent to monitor and reduce the excess mortality by windfarm collisions that threatens this lesser kestrel population. More generally, we advocate the use of population matrix demographic models in impact assessment studies to avoid placing new windfarms close to rare species that could not sustain additional mortality by collisions.

A novel 2D analytical model for predicting single and multiple‐interacting wakes of horizontal‐axis wind turbines

AbstractA 2D analytical model has been proposed for predicting the wind velocity distribution inside the wake by coupling the Jensen wake model and the Gaussian function using the conservation of mass and momentum. More realistic approaches were adopted in choosing the control volume and calculation of the wake initial radius at the formation and a new modified expression was employed for the wake turbulence intensity to be used for computation of the revised wake decay. Validation of the model was performed using a body of experimental and numerical data, where the model was thoroughly compared with five analytical models of the literature. The model was successful in predicting double‐interacting wakes, and gave more accurate single wake velocity profiles at the far wake region, especially at downwind distances of ( is the rotor diameter), which are the economically‐ and power harvesting‐optimized spacing between the turbines in wind farms.