Future Wind Turbines Research Articles

Extrapolation is not enough: impacts of extreme land use change on wind profiles and wind energy according to regional climate models

Abstract. Humans change climate in many ways. In addition to greenhouse gases, climate models must therefore incorporate a range of other forcings, such as land use change. While studies typically investigate the joint effects of all forcings, here we isolate the impact of afforestation and deforestation on winds in the lowermost 350 m of the atmosphere to assess the relevance of land use change for large-scale wind energy assessments. We use vertically resolved sub-daily output from two regional climate models instead of extrapolating near-surface winds with simplified profiles. Comparing two extreme scenarios, we report that afforestation reduces wind speeds by more than 1 m s−1 in many locations across Europe, even 300 m above ground, underscoring its relevance at hub heights of current and future wind turbines. We show that standard extrapolation with modified parameters approximates long-term means well but fails to capture essential spatio-temporal details, such as changes in the daily cycle, and it is thus insufficient to estimate wind energy potentials. Using adjacent climate model levels to account for spatio-temporal wind profile complexity, we report that wind energy capacity factors are strongly impacted by afforestation and deforestation: they differ by more than 0.1 in absolute terms and up to 50 % in relative terms. Our results confirm earlier studies showing that land use change impacts on wind energy can be severe and that they are generally misrepresented with common extrapolation techniques.

Investigation of the large-diameter monopiles response under flow-controlled loading

The mechanical behavior of monopile support systems for offshore wind turbines under current-induced loads was investigated through numerical simulations. Using the Large Eddy turbulence model, the fluid-structure coupling was analyzed in both unidirectional and bidirectional directions. In the unidirectional coupling, the pressure and velocity profiles of the fluid around the pile were determined. While in the bidirectional coupling, the force distribution, pile displacement, and moment distribution were obtained, along with the current-induced load transmitted from the pile to the soil. The coupling analysis revealed that the fluid flow around the pile exhibited cyclic loading behavior due to the interaction between the fluid and the pile, effectively resulting in an oscillating pile within a steady flow. Additionally, the pile’s stress distribution remained within the tensile yield limit of the steel, indicating a stable state in the fluid-pile model within the given flow conditions. Furthermore, the soil reaction forces obtained from the fluid-pile-soil coupling model validated the accuracy of the current-induced load calculations. This study introduces a novel approach that considers the fluid-pile-soil coupling, offering valuable insights for pile foundation design. The findings of this research have significant engineering implications and practical value, providing a robust foundation for future offshore wind turbine installations.

Bend-twist adaptive control for flexible wind turbine blades: Principles and experimental validation

This study proposes a new methodology for optimizing the power curve of a wind turbine at low wind speeds. The principles of bend-twist coupling and the mechanism of energy exchange between the structure and inflow are analyzed. For the blade’s geometric nonlinearity, the virtual displacements and strain fields are described using the Green-Lagrange strain theorem, retaining third-order terms in the energy expressions. The equations of motion are derived using Hamilton’s principle. The bend-twist coupling effects and large deformations of the blade are analyzed using the Updated Lagrange method. Notably, the angle of attack for a single blade section is influenced by bend-twist deformation, causing variations in the rotor’s maximum power coefficient from its optimal value. Additionally, the projection length of the blade, influenced by centrifugal forces, also affects the bend-twist deformation. Based on these findings, an aero-elastic coupling control strategy, termed “Bend-twist Adaptive Control”, is proposed and validated through experiments. The results demonstrate that the proposed control strategy could increase annual power production by 2.3 %. These conclusions offer a promising outlook for future wind turbine blade design and power optimization.

Hierarchical sensitivity study on the aeroelastic stability of the IEA 15 MW reference wind turbine

Increased rotor size and blade flexibility are leading to new stability characteristics for current and future wind turbines. Numerical aeroelastic models are an essential tool to understand these new mechanisms and to design next-generation wind turbines suitably. A comprehensive understanding of how model input parameters influence the stability analysis will enhance the confidence in these simulations. This article presents a study on the sensitivity of uncertain parameters on aeroelastic stability predictions of the IEA 15 MW turbine model in HAWCStab2. It uses a hierarchical approach to handle the large number of investigated model parameters. Relevant uncertainties are identified through one-at-a-time and elementary effects analyses first. The remaining parameters are fed into a variance-based uncertainty quantification (UQ), that employs polynomial chaos expansion representations as surrogates for a full nonlinear description of the sensitivities. A robust post-processing of the stability analysis results is the main challenge of the presented methodology, but it is shown how the UQ process can still be used to explore the parameter space effectively. The presented study shows that the structural blade properties have the highest sensitivity and that a set of relatively small parameter variations can lead to unstable behavior of the reference model.

Floaters upscaling for multi-megawatt floating wind turbines

The floating wind industry requires wind turbines with increasing power ratings, which will require bigger platforms. However, few floater concepts are publicly available for numerical simulation of future wind turbines. To overcome this limit, this work proposes a fast and straightforward upscaling methodology to upscale already available floating platforms to greater wind turbine power ratings. The methodology does not aim to optimize the floater design, but it can generate realistic floater data for preliminary numerical simulations. The methodology is applied here to four floaters: a semisubmersible, a barge, a TLP and a spar. The platforms are upscaled for a 15 MW wind turbine and a specific deployment site. The upscaling methodology is adapted for each floater since some differences arise from one platform to the other. Whenever possible a comparison with already existing 15 MW substructures is proposed and commented.

Feasibility study on preloaded flexible conical plain bearings as wind turbine main bearings

Wind turbines (WT) are a key technology towards a carbon neutral energy production worldwide. Currently about 30 % of the Levelized Cost of Electricity (LCoE) consist of service and maintenance. Critical components are the main roller bearings which have a failure probability between 15 and 30 % within 20 years. Main bearing replacements are expensive maintenance procedures because an external crane is needed which cost about $250.000 per day for offshore WTs. Hence, a discussed countermeasure within the wind industry is to use segmented plain bearings in future WTs. These types of bearings allow an up-tower sub-component wise replacement of faulty parts without the need to dismantle the whole drivetrain. Therefore, the Chair for Wind Power Drives (CWD) developed, tested and validated a segmented conical plain bearing concept for the use as a main bearings for WTs. To function properly common plain bearings need a minimum clearance to allow the formation of a convergent lubrication gab. This initial clearance negatively influences run-out, as the shaft con move freely within the limits of the clearance. As WT drivetrain concepts become more integrated, main bearings for WTs need to fulfill higher requirements regarding the allowable run-out of the main shaft. Therefore, a fundamental design challenge arises for plain bearings as rotor main bearings. One approach to reduce run-out for roller main bearings is to use preloaded tapered roller bearings. For common plain bearings however preloading is not possible. However, the concept of preloading was successfully transferred to the flexible conical plain bearing concept developed at the CWD and the main shaft run-out severely reduced. In this work the feasibility of a preloaded flexible conical plain bearing as a WT rotor main bearing is evaluated and the advantages and disadvantages contrasted.

Active trailing edge flap system fault detection via machine learning

Abstract. Active trailing edge flap (AFlap) systems have shown promising results in reducing wind turbine (WT) loads. The design of WTs relying on AFlap load reduction requires implementing systems to detect, monitor, and quantify any potential fault or performance degradation of the flap system to avoid jeopardizing the wind turbine's safety and performance. Currently, flap fault detection or monitoring systems are yet to be developed. This paper presents two approaches based on machine learning to diagnose the health state of an AFlap system. Both approaches rely only on the sensors commonly available on commercial WTs, avoiding the need and the cost of additional measurement systems. The first approach combines manual feature engineering with a random forest classifier. The second approach relies on random convolutional kernels to create the feature vectors. The study shows that the first method is reliable in classifying all the investigated combinations of AFlap health states in the case of asymmetrical flap faults not only when the WT operates in normal power production but also before startup. Instead, the second method can identify some of the AFlap health states for both asymmetrical and symmetrical faults when the WT is in normal power production. These results contribute to developing the systems for detecting and monitoring active flap faults, which are paramount for the safe and reliable integration of active flap technology in future wind turbine design.

Advances and opportunities in wind energy harvesting using plasma actuators: a review

Abstract The dielectric barrier discharge plasma actuator has been recognized as a leading technology for controlling fluid flow and has found remarkable applications in wind energy harvesting over the past decade. Wind turbine aerodynamics are critical in this concept and performance is mainly determined by flow controllers, although significant technical progress is still required. This paper examines all the critical studies to investigate the potential application of plasma actuators for airflow control over wind turbines. This approach has been divided into three categories: wind turbine airfoils, horizontal-axis wind turbines and vertical-axis wind turbines aerodynamic performance and generated power. Finally, the potential functions of plasma actuators in current and future wind turbine generators are discussed. These actuators offer promising solutions to increasing power output, minimizing torque fluctuations and enabling self-starting capabilities, particularly in vertical-axis wind turbines. By adjusting blade pitch angles in conjunction with plasma actuators, significant improvements in airflow optimization and power extraction have been demonstrated. Despite the advancements, challenges persist, such as determining optimal actuator placement and overcoming structural limitations, especially concerning 3D effects and high Reynolds numbers. While plasma actuators enhance aerodynamic efficiency, their complexity needs to be balanced against marginal gains in power production, especially in high-megawatt turbines, for which controlling flow at low wind speeds is challenging. Future research must focus on the sustainable integration of plasma actuators, pitch angle adjustments and active control mechanisms to fully exploit the potential of wind energy for a sustainable future.

3D CFD study of a DeepWind demonstrator at design, off-design and tilted operating conditions

Energy transition, towards increased renewables, demands for reliable, efficient, and innovative technical solutions, at acceptable cost. Wind energy conversion exhibits one of the greatest potential, mainly in off-shore deployment. Vertical-axis wind turbines are characterized by a reduced wave recovery and enhanced power output, which boost the installation of bigger capacity turbines, properly cluster to maximize the farm density. These two requirements entail deeper understanding of the wake physics and detailed description of the machine performance. A careful 3D CFD investigation, supported by experimental validation, is carried out to define the relevant flow mechanism of a lab-model, DeepWind demonstrator, in upright and tilted condition. The results show how the performance is varying along the span, and how it is affected by a skewed flow. The lower part of the machine benefits from combined effect of blade curvatures and rotor inclination. A thorough description of the complex vortical field complements the performance data, and provide useful considerations apt for promoting the design of future vertical-axis wind turbines, for floating off-shore applications. The performance parameters are then computed for a full-size rotor to show how the Reynolds effect play a relevant role in the machine aerodynamics of bigger capacity turbines.

The inclusion of uncertainty in circularity transition modeling: A case study on wind turbine blade end-of-life management

The transition to a more circular economy (CE) is complex and hard to predict, including in sustainable energy technologies. Many sources of uncertainty make it challenging to model CE scenarios and their potential benefits. As an example, the high variability in costs and revenues of different recycling options makes future wind turbine blade recycling highly uncertain. To better understand this challenge, the circular economy, life cycle assessment and visualization (CELAVI) framework – a discrete event simulation and life cycle assessment framework – is modified to incorporate uncertainty analysis capabilities. Moreover, a 3-step procedure that covers different aspects of uncertainty in CE studies and includes a Monte-Carlo analysis is proposed. The procedure is tested in a case study on wind turbine blade recycling using CELAVI. Results highlight that grinding and landfilling costs are the most influential parameters for wind power circularity. The model’s output coefficients of variation (when input parameter uncertainties are propagated) are between 92% and 384% depending on the indicator. The approach developed in this study may help researchers and decision-makers who study circularity prioritize their data collection effort. Finally, our method contributes to a mounting yet critical body of research: the measurement of uncertainty in circularity transitions.

Towards smart blades for vertical axis wind turbines: different airfoil shapes and tip speed ratios

Abstract. Future wind turbines will benefit from state-of-the-art technologies that allow them to not only operate efficiently in any environmental condition but also maximise the power output and cut the cost of energy production. Smart technology, based on morphing blades, is one of the promising tools that could make this possible. The present study serves as a first step towards designing morphing blades as functions of azimuthal angle and tip speed ratio for vertical axis wind turbines. The focus of this work is on individual and combined quasi-static analysis of three airfoil shape-defining parameters, namely the maximum thickness t/c and its chordwise position xt/c as well as the leading-edge radius index I. A total of 126 airfoils are generated for a single-blade H-type Darrieus turbine with a fixed blade and spoke connection point at c/2. The analysis is based on 630 high-fidelity transient 2D computational fluid dynamics (CFD) simulations previously validated with experiments. The results show that with increasing tip speed ratio the optimal maximum thickness decreases from 24 %c (percent of the airfoil chord length in metres) to 10 %c, its chordwise position shifts from 35 %c to 22.5 %c, while the corresponding leading-edge radius index remains at 4.5. The results show an average relative improvement of 0.46 and an average increase of nearly 0.06 in CP for all the values of tip speed ratio.

Adaptive Flexible Controller Frequency Optimization for Reducing Structural Response of a 10 Mw Floating Offshore Wind Turbine

This study presents the adaptive flexible controller frequency optimization method (AFCFOM) as a means of minimizing dynamic loads and structural response in large-scale floating wind turbines, with the goal of enhancing reliability and reducing the frequency of failures. AFCFOM employs a flexible proportional integral (PI) controller frequency, allowing the wind turbine to autonomously adapt to different wind and wave conditions while minimizing structural response. The AFCFOM framework utilizes Latin hypercube sampling for spatial compression, two Bayesian neural networks (BNN) for training and mapping, and particle swarm optimization (PSO) for controller frequency seeking, along with the extreme response surface method for processing the response dataset. OpenFSAT was used for raw data collection and method effect validation. The simulation results demonstrate that the application of AFCFOM effectively reduces the extreme values and variance of the axial displacement at the blade top of the floating offshore wind turbine blade and enhances the robustness of the floating platform. The study provides an overview of the potential effects of adaptive flexible controller frequency on wind turbines, serving as a reference for future wind turbine control optimization research.

Grand challenges in the design, manufacture, and operation of future wind turbine systems

Abstract. Wind energy is foundational for achieving 100 % renewable electricity production, and significant innovation is required as the grid expands and accommodates hybrid plant systems, energy-intensive products such as fuels, and a transitioning transportation sector. The sizable investments required for wind power plant development and integration make the financial and operational risks of change very high in all applications but especially offshore. Dependence on a high level of modeling and simulation accuracy to mitigate risk and ensure operational performance is essential. Therefore, the modeling chain from the large-scale inflow down to the material microstructure, and all the steps in between, needs to predict how the wind turbine system will respond and perform to allow innovative solutions to enter commercial application. Critical unknowns in the design, manufacturing, and operability of future turbine and plant systems are articulated, and recommendations for research action are laid out. This article focuses on the many unknowns that affect the ability to push the frontiers in the design of turbine and plant systems. Modern turbine rotors operate through the entire atmospheric boundary layer, outside the bounds of historic design assumptions, which requires reassessing design processes and approaches. Traditional aerodynamics and aeroelastic modeling approaches are pressing against the limits of applicability for the size and flexibility of future architectures and flow physics fundamentals. Offshore wind turbines have additional motion and hydrodynamic load drivers that are formidable modeling challenges. Uncertainty in turbine wakes complicates structural loading and energy production estimates, both around a single plant and for downstream plants, which requires innovation in plant operations and flow control to achieve full energy capture and load alleviation potential. Opportunities in co-design can bring controls upstream into design optimization if captured in design-level models of the physical phenomena. It is a research challenge to integrate improved materials into the manufacture of ever-larger components while maintaining quality and reducing cost. High-performance computing used in high-fidelity, physics-resolving simulations offer opportunities to improve design tools through artificial intelligence and machine learning, but even the high-fidelity tools are yet to be fully validated. Finally, key actions needed to continue the progress of wind energy technology toward even lower cost and greater functionality are recommended.

Forecasting suitable areas for wind turbine occurrence to proactively improve wildlife conservation

The United States is rapidly expanding production of renewable energy to meet increased energy demands and reduce greenhouse gas emissions. Wind energy is at the forefront of this transition. A central challenge is understanding the nexus between wind energy development and its capacity for negative effects on wildlife causing population declines and habitat loss. Collaboration among conservationists and developers, early in the planning process, is crucial for minimizing wind-wildlife conflicts. Such collaborations require data showing where wind and wildlife impacts occur. To meet this challenge and inform decision-making, we provide natural resource agencies and stakeholders information regarding where future wind turbines may occur, and the potential affects on natural resource management, including the conservation of priority species and their habitats. We developed a machine learning model predicting suitability of wind turbine occurrence (hereafter, wind turbine suitability) across an eight-state region in the United States, representing some of the richest areas of wind potential. Our model incorporates predictor variables related to infrastructure, land ownership, meteorology, and topography. We additionally created a constraint layer indicating areas where wind would likely not be developed because of zoning, protected lands, and restricted federal agency proximity guidelines. We demonstrate how the predictive wind turbine suitability model informs conservation planning by incorporating animal movement models, relative abundance models coupled with spatial conservation planning software, and population density models for three exemplar, high priority species often affected by wind energy: whooping cranes (Grus americana), golden eagles (Aquila chrysaetos), and lesser prairie-chickens (Tympanuchus pallidicinctus). By merging the wind turbine and biological models, we identified conservation priority areas (i.e., places sharing high suitability for wind turbines and species use), and places where wind expansion could minimally affect these species. We use our “species-wind turbine occurrence relationships” to demonstrate applications, illustrating how forecasting areas of wind turbine suitability promotes wildlife conservation. These relationships inform wind energy siting to reduce negative ecological impacts while promoting environmental and economic viability.

The Hydrogen Wind Turbine: Design of a wind turbine optimised for hydrogen production

“Green” hydrogen produced using electrolysis and zero-carbon electricity may be a path for the EU to reduce its dependence on natural gas imports and support the green transition. The EU uses 10 Mt of natural gas yearly for hydrogen production, representing a potential 120 GW market for the wind industry. Placing the hydrogen production (electrolysers) near the energy source (wind turbines) instead of the consumer (e.g. fertiliser producers) changes the energy transportation from power to hydrogen and opens new design possibilities. This work investigates how an off-grid wind turbine designed for hydrogen production may differ from a traditional one.The paper first describes the numerical modelling framework used to estimate the hydrogen production and cost of a “hydrogen wind turbine.” The main parameters of the system (generator rating, rotor diameter, electrolyser rating and battery capacity) are then optimised to reduce the levelised cost of hydrogen (LCOH). Results show that an LCOH-optimised wind turbine should have a lower specific power (i.e. a larger rotor) than an LCOE-optimised design. We find a 7% LCOH reduction compared to the reference system, opening the door for future wind turbines designed directly for hydrogen production.

Resources time footprint analysis of onshore wind turbines combined with GIS-based site selection: A case study in Fujian Province, China

Wind power is a promising renewable energy source that is rapidly expanding in the global electricity mix. This study explored suitable locations for onshore wind turbines, and estimated the wind power potential in Fujian Province, China. We also analyzed the resource occupancy of 100 years of potential wind turbine operation in terms of the materials, CO2 emissions, land, and labor. This was done using the sustainability indicator – resources time footprint (RTF). RTF measures the ratio of the occupancy of the resource within a period of time to the total capacity to provide a product or service. This is done using a time dimension (years). A higher RTF value indicates a larger environmental and social impact. Fujian Province has an annual wind power potential of 50,638 GWh, which is mainly clustered around its coastal regions. The RTF of steel is the largest in the material aspect, which is 1.2 times that of copper and 23.9 times that of aluminum. Negative values of the RTF of CO2 and land show that wind power is conducive to CO2 emission reduction and the prevention of future land occupancy. Furthermore, the construction and operation of wind turbines will not impose a burden on the labor force. The RTF values of the wind turbines decreased with an increase in the annual average wind speed. By modifying the parameters, this methodology can be applied to determine ideal locations in other regions, which present the highest environmental benefits, for future wind turbine installations.

Effects of land-based wind turbine upsizing on community sound levels and power and energy density

Multiple technological, social, and market factors of wind power are evolving rapidly. Most notably, significant wind turbine scaling is occurring and is forecasted to continue. While the larger turbines expected to be deployed in the future are more powerful and efficient, they are also expected to operate at higher sound levels and require larger setbacks than those installed in the last decade. These sometimes-competing deployment trends and impacts cannot be understood via simple extrapolations of past trends. This study analyzes the effect of these future larger turbines on wind turbine micro-siting, project-level power and energy density, and community noise impacts. Due to their taller heights, larger rotors, and higher sound power levels, future wind turbines will require larger setbacks from homes and greater inter-turbine spacing, resulting in fewer turbines deployed for a given land area. This research finds these changes more than offset the effect of the higher turbine sound emissions, significantly decreasing the average sound levels that wind plant hosting communities experience. Yet, simultaneously, plant layouts using future turbine designs also result in projects with higher installed capacities and annual energy output for a given land area. These increases will likely lead to increased tax benefits and local income in the community. The deployment of fewer turbines on a smaller number of parcels could have implications on siting flexibility and landowner payments.

Wireless Real-Time Data Acquisition System for Aerodynamic Characterization of a Wind Turbine

Context: Wind energy sources utilize renewable, free, and environmentally friendly resources, so there are projects focused on the design of wind turbines to increase their efficiency. Simulations using computational fluid dynamics techniques allow designs to be analyzed. which, are then validated through experimentation and wind tunnel measurements. Method: Data acquisition is performed by using Wind Sensor Rev.P sensors to measure wind velocity, and MMA7361 accelerometer to measure the vibrations generated by the wind in the turbine. Additionally, an ESP32 is required to process and transmit the data via Bluetooth, thus facilitating the installation of the real-time monitoring module inside the wind tunnel. Results: A monitoring module with a sampling rate of 5 ms was obtained, thus allowing real-time data acquisition, with a resolution of 12 bits for good data capturing accuracy and a transmission rate of approximately 74000 bps. An application capable of plotting 3D acceleration and 2D wind speed signals was also created. Conclusions: The device developed serves as a support for future wind turbine design projects since it is wireless, its installation is simple, it facilitates data collection inside a wind tunnel, and it allows visualizing the turbine’s behavior in real time by means of a graphic interface.

Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review

As grid-connected wind farms become more common in the modern power system, the question of how to maximize wind power generation while limiting downtime has been a common issue for researchers around the world. Due to the complexity of wind turbine systems and the difficulty to predict varying wind speeds, artificial intelligence (AI) and machine learning (ML) algorithms have become key components when developing controllers and control schemes. Although, in recent years, several review papers on these topics have been published, there are no comprehensive review papers that pertain to both AI and ML in wind turbine control systems available in the literature, especially with respect to the most recently published control techniques. To overcome the drawbacks of the existing literature, an in-depth overview of ML and AI in wind turbine systems is presented in this paper. This paper analyzes the following reviews: (i) why optimizing wind farm power generation is important; (ii) the challenges associated with designing an efficient control scheme for wind farms; (iii) a breakdown of the different types of AI and ML algorithms used in wind farm controllers and control schemes; (iv) AI and ML for wind speed prediction; (v) AI and ML for wind power prediction; (vi) AI and ML for mechanical component monitoring and fault detection; and (vii) AI and ML for electrical fault prevention and detection. This paper will offer researchers and engineers in the wind energy generation field a comprehensive review of the application of AI and ML in the control methodology of offshore and onshore wind farms so that more efficient and robust control schemes can be designed for future wind turbine controllers.

Preliminary Sizing and Optimization of Semisubmersible Substructures for Future Generation Offshore Wind Turbines

Several key development areas have been identified as having high potential for reducing the levelized cost of energy of offshore wind. Two of the most anticipated developments are future generation large wind turbines and the use of floating foundations. There is thus a need for developing large floating substructures that are capable of hosting future generation wind turbines. This work presents the preliminary sizing of two semi-submersible platforms for supporting a 25 MW turbine through a design space search using a simplified parametric analysis. Compared to simple theoretical upscaling, the substructures resulting from the proposed simplified parametric analysis have significantly lower steel mass and stiffer tower.