Wind Turbine Hub Research Articles

Data-driven optimization of material composition and casting processes for large ferritic nodular cast iron components in wind turbines

Abstract This study addresses the challenges of producing large-scale wind turbine components by proposing an optimization scheme for the material properties and casting processes of ductile cast iron. The study integrates experimental data with JMatPro calculations to establish a comprehensive database of 1, 139 ferritic nodular cast iron samples. Using machine learning and multi-objective optimization, an optimized composition was developed, and Pareto front solutions were achieved with performance significantly surpassing QT400-18. The AI-CAST platform was then applied to refine the casting process for a 6 MW wind turbine hub, reducing internal defects to 2, 738.52 mm³ and decreasing loose defects by 83.68%. This integrated approach not only enhances material performance and casting quality but also offers significant cost savings, suggesting broad applicability for improved production efficiency in the casting industry.

An effective sizing study on PV-wind-battery hybrid renewable energy systems

Hybrid renewable energy system is the effective and efficient way of energy harvesting in remote or isolated areas having the advantage of harnessing of multiple renewable sources at a time. Solar, wind and battery-based hybrid system is one of the most promising alternatives in this field. Sizing of the system components depend on various technical and economical specifications. The research studied the effect of solar panel tilt angle and wind turbine hub height on sizing optimization of PV wind battery-based hybrid renewable energy system using HOMER software. Through empirical method, an optimal monthly, seasonal, yearly optimal tilt angle and wind turbine hub height have been computed. These values are used in addition with various other technical and economical specifications in order to determine the optimal sizing of hybrid system. At first, three different scenarios have been considered for the study of yearly, seasonal, and monthly optimal tilt angle with an optimized hub height for the wind turbine. Then, a comparative analysis of the sizing of hybrid system at unoptimized and optimized tilt angle and hub height have also been done. Furthermore, a detailed comparative and sensitivity analysis have also been performed across different scenarios and approaches. A detailed financial study for policy planning and implementation issues with other, traditional state of the art approaches significantly demonstrates the effectiveness of the proposed method for optimal sizing of the hybrid system.

Research on the Effect of Thermal Coating Temperature on the Fatigue Performance of Wind Turbine Blade Bolts

The bolted connection of wind turbine blades is a bridge for transmitting blade loads. In recent years, with the rapid increase in blade length, the fatigue load at the blade root has also been continuously increasing. The bolt fatigue strength has always been a bottleneck in the interface design between blade and wind turbine hub. However, industry standards still hold conservative values for the S/N curve of bolt fatigue, and there are unclear requirements for thermal coatings, The S/N curves provided by the standards in different periods do not meet the actual processing requirements of bolts. In order to study the effect of thermal coating temperature on the fatigue performance of threads and provide a reasonable basis for the subsequent design of long blade bolt connections. Based on the actual processing technology of bolts, experiments were conducted on the S/N fatigue curves of thermal coated bolts at different temperatures. The experimental results prove that the slope of the inclined part of the low-temperature Dacromet test data and the fatigue strength under 2 million cycles are significantly higher than normal temperature Dacromet coated bolts. Low temperature Dacromet can effectively improve the fatigue strength of bolts.

Study on Dynamic Response Characteristics of Offshore Floating Wind Turbine Pitch System

Due to its special working conditions, offshore wind turbine will bear large direct and indirect loads under the combined action of air flow and wave flow. In this paper, a variable pitch system composed of variablepitch motorand variable pitch bearing is improved, and the characteristics of system's bending moment, torque, vibration and other physical quantities under the action of multiple physical loads are verified, and the mechanical response characteristics of floating wind turbine under the control of unified variable pitch and independent variable pitch are studied under the running conditions at sea. The results show that mechanical structure of uniform pitch is compared with that of independent pitch, the independent variable pitch structure can effectively reduce the mean oscillation value of wind turbine tower in the parallel direction of air flow by optimizing control strategy, and reduce the thrust at the hub of wind turbine and the bending moment at the root of tower, but increase the vibration frequency and fatigue load of offshore wind turbine tower along parallel direction of air flow. Reduce the fatigue life of equipment. The research results can be used as a reference to reduce the variable pitch control and vibration suppression of offshore wind turbines and improve the reliability of wind turbines.

Relevance Of Era5 Reanalysis For Wind Energy Applications: Comparison With Sodar Observations

ERA5 reanalysis is one of the most trusted climate data sources for wind energy modeling. However, any reanalysis should be verified through comparison with observational data to detect biases before further use. For wind verification at heights close to typical wind turbine hub heights (i.e. about 100 m), it is preferable to use either in-situ measurements from meteorological towers or remote sensing data like acoustic and laser vertical profilers, which remain independent of reanalysis. In this study, we validated the wind speed data from ERA5 at a height of 100 m using data from four sodars (acoustic profilers) located in different climatic and natural vegetation zones across European Russia. The assessments revealed a systematic error at most stations; in general, ERA5 tends to overestimate wind speed over forests and underestimate it over grasslands and deserts. As anticipated, the largest errors were observed at a station on the mountain coast, where the relative wind speed error reached 45%. We performed the bias correction which reduced absolute errors and eliminated the error dependence on the daily course, which was crucial for wind energy modeling. Without bias correction, the error in the wind power capacity factor ranged from 30 to 50%. Hence, it is strongly recommended to apply correction of ERA5 for energy calculations, at least in the areas under consideration..

Reconstructing turbulent wind-fields using inverse-distance-weighting interpolation and measurements from a pulsed mounted-hub lidar

This study evaluates a numerical multi-beam pulsed lidar mounted on the hub of the NREL 5MW reference wind turbine using the HAWC2 v13.1 numerical sensor for synthetic lidar measurement generation. While initially designed for single-beam operations, it facilitates multi-beam configuration simulations. We conducted an analysis of full-rotor longitudinal wind speed reconstruction by combining inverse-distance-weighting with synthetic sensor data from HAWC2. Utilizing a Mann-generated turbulence box for wind input at U = 11.4 m/s, we examined three lidar configurations for efficacy. The hub-mounted lidar proved efficient in capturing the incoming flow towards the turbine, showing a 60% improvement in overall reconstruction accuracy across the plane compared to the baseline, where hublidar measurements are simple average across the rotor plane. The rotor average wind speed showed a 30% enhancement compared to the baseline. Crucially, the lidar configuration, which impacted the spatial distribution across the rotor plane, emerged as a pivotal factor for effective reconstruction. Proper configuration assessment is essential, especially given the implications of rotational sampling and its impact under various wind conditions, for optimal performance. The proposed method, combining inverse-distance weighting with hub-lidar data for high spatial resolution measurements across the rotor plane, shows significant potential for real-time windflow estimation and lidar-assisted control applications.

Optimization of Non-Uniform Onshore Wind Farm Layout Using Modified Electric Charged Particles Optimization Algorithm Considering Different Terrain Characteristics

Designing an onshore wind farm layout poses several challenges, including the effects of terrain and landscape characteristics. An accurate model should be developed to obtain the optimal wind farm layout. This study introduces a novel metaheuristic algorithm called Modified Electric Charged Particles Optimization (MECPO) to maximize wind farms’ annual energy production (AEP) by considering the different terrain and landscape characteristics of the sites. Some non-uniform scenarios are applied to the optimization process to find the best combination of decision variables in the wind farm design. The study was initiated by a uniform wind farm layout optimization employing identical wind turbine hub heights and diameters. Following this, these parameters underwent further optimization based on some non-uniform scenarios, with the optimal layout from the initial uniform wind farm serving as the reference design. Three real onshore sites located in South Sulawesi, Indonesia, were selected to validate the performance of the proposed algorithm. The wind characteristics for each site were derived from WAsP CFD, accounting for the terrain and landscape effects. The results show that the non-uniform wind farm performs better than its uniform counterpart only when using varying hub heights. Considering the impacts of the terrain and landscape characteristics, it is observed that sites with a higher elevation, slope index, and roughness length exhibit a lower wake effect than those with lower ones. Moreover, the proposed algorithm, MECPO, consistently outperforms other algorithms, achieving the highest AEP across all simulations, with a 100% success rate in all eight instances. These results underscore the algorithm’s robustness and effectiveness in optimizing wind farm layouts, offering a promising avenue for advancing sustainable wind energy practices.

Effectiveness of installing multiple tuned mass dampers for seismic mitigation of steel–concrete wind turbine hybrid tower

The concept of a steel–concrete hybrid tower (SCHT) has recently been proposed to satisfy the demand for wind turbine hub higher than 120 m. This study details the effect of tuned mass dampers (TMDs) on the seismic mitigation of SCHTs. The equation of motion of the coupled system was formulated using a multi-degree-of-freedom system equivalent to the SCHT. The optimum properties of the TMDs were identified to control the tower multimode responses using the frequency-domain approach. Finally, time-history analyses were conducted to evaluate the effectiveness of the optimised TMDs on the SCHT under an ensemble of 22 historical earthquakes. The reasons why the TMDs cannot always achieve the expected control performance for the SCHT are explained.

Ensemble Bayesian Model Averaging Projections of Wind‐Speed Extremes for Wind Energy Applications Over China Under Climate Change

AbstractWind energy has grown rapidly in recent years as a measure to control carbon emissions and mitigate climate change. Extreme wind can damage wind turbines, cause losses to wind power plants, limit economic benefits of wind energy facilities, and disrupt regional grid balance. Therefore, an accurate assessment of extreme wind speeds at wind turbine hub height and their spatiotemporal variation under climate change is critical for the planning of wind energy and for guaranteeing regional energy security. In this study, the 100‐m extreme wind speeds in China are estimated using an empirical downscaling and Bayesian model averaging ensemble method with the latest ERA5 reanalysis and 20 global climate models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6). Two shared socioeconomic pathways (SSP), that is, SSP2‐4.5 and SSP5‐8.5, are considered to account for the uncertainty in anthropogenic emissions. According to the results, the highest extreme wind speeds are primarily found in Inner Mongolia, northeast China, western Tibet, and the eastern coastal region. Extreme wind speeds in central and southeastern China are projected to increase by approximately 2% in the middle (2031–2060) and the end (2071–2100) of the 21st century relative to the baseline period (1985–2014). Summer extreme wind speeds in northwestern Tibet are expected to increase by more than 9% at the end of the century. The findings of this study indicate that it is important to take the present and projected changes in local wind extremes into account when choosing locations for wind power plants and wind energy installations.

Techno-economic feasibility of stand-alone hybrid energy system with battery storage in educational buildings: A case study of Uttara University

Stand-alone hybrid energy systems (HES) have the potential to significantly reduce pollutant emissions and alleviate strain on the national grid. The selection and sizing of stand-alone HES for buildings can serve as a methodological approach toward establishing a resilient and clean electrical energy system in urban areas of developing countries. To explore this further, a case study was conducted using Hybrid Optimization of Multiple Energy Resources (HOMER) to assess the feasibility of implementing a HES at Uttara University in Dhaka, Bangladesh. This study examined the technical, economic, environmental, social, and reliability aspects of a stand-alone HES. The findings of this study suggested that the PV-WT-DG-BT is the most cost-effective solution for the University when compared to other hybrid configurations. Additionally, this HES benefits the surrounding communities by creating employment opportunities and contributing to human development. The sensitivity analysis showed that the COE and NPC of the optimal HES are sensitive to fuel price changes. A PV slope angle of 23.88° was cost-effective, and a 20-meter wind turbine hub height was optimal. It was observed that increased solar radiation decreases COE. Further, sensitivity analysis showed that fuel costs, CO2 emissions, and NPC will decrease if average daily load demand declines. It was also found that 1 kWh of Li-Ion-based HES performed the best compared to other batteries. Finally, the results of the genetic algorithm (GA), and grey wolf optimizer (GWO) showed that they provide the same optimum configuration as HOMER but with the lowest NPC. The findings of this study can serve as a foundational model for designing HESs in other large educational buildings located in low and middle-income countries with environmental conditions and socio-economic characteristics similar to Bangladesh.

Identifying wind turbines from multiresolution and multibackground remote sensing imagery

The wind energy industry has expanded in recent years. Promotion of the Sustainable Development Goals (SDGs) is expected to further increase the scale of the wind energy industry. Determining the location and quantity of wind turbines is crucial for monitoring the development status of the wind energy industry and evaluating wind energy production. In this study, we propose a method for simultaneously detecting and positioning wind turbines in remote sensing images, namely, Wind Turbine YOLO (WT-YOLO), based on the You Only Look Once version 5 model (YOLOv5). The wind turbine hub, base, and shadow hub are treated as key points in the proposed method. Regression terms are incorporated into the head of the YOLOv5 basic framework to predict the location of these three key points. The base point is utilized to determine the exact position of the wind turbine. A multibackground and multiresolution wind-turbine image dataset is constructed by sampling high-resolution images from Google Earth. The WT-YOLO method outperforms existing methods in both wind turbine detection and positioning on different types of land cover backgrounds and multiresolution images of the constructed dataset. In the spatial resolution range of 0.6 m to 5.4 m, WT-YOLO exhibits enhanced wind-turbine detection, where the average precision (AP) is 5.92 % to 15.43 % higher than that of existing wind-turbine detection methods. Wind-turbine positioning by WT-YOLO has a mean distance error (MDE) that is 16.06 m to 21.59 m lower than that of existing wind-turbine positioning methods. A comparative analysis showed that the shadow and the three key points are effective features for wind-turbine detection. The proposed WT-YOLO model can support detection, positioning and counting for wind turbines worldwide.

Design and realization of a pre-production platform for wind turbine manufacturing

Addressing the challenge of rehearsing large-scale equipment assembly, particularly for oversized components like wind turbine towers, hubs, nacelles, and blades, which often face quality issues such as substandard workmanship and wide tolerances during on-site assembly, a system has been developed for simulating wind turbine assembly. This system enables digital wind turbine assembly by creating virtual and production process models and employing intelligent database analysis. It resolves the problem of the absence of pre-production for large-scale equipment, meets the batch production needs of the wind turbine manufacturing industry, enhances the safety of wind turbine operations, improves operator assembly skills, and boosts production efficiency. This platform has already been implemented in the wind turbine manufacturing industry, yielding significant economic benefits.

Wind resource assessment and potential development of wind farms along the entire coast of South Korea using public data from the Korea meteorological administration

The feasibility of a wind farm locations should be evaluated on a thorough analysis and assessment of the available wind resources which necessitates wind data measurement for a minimum period of one year. Nevertheless, several limitations arise during this process, including challenges related to the site selection, cost considerations such as the installation of meteorological masts subsequent to marine physical and geotechnical surveys, and the requirement for ongoing maintenance. Therefore, several studies have been carried out to determine wind energy potential using reanalysis data. However, in areas with distinct terrain characteristics, such as maritime and coastal areas, uncertainties exist in the use of reanalysis data. Therefore, an analysis of wind resources based on measurement data is necessary in such areas. In this study, wind data measured by the Korea Meteorological Administration's automatic weather system and light house automatic weather system for 56 points along the Korean coast is obtained and analyzed. Additionally, the expected annual energy production and capacity factor were predicted when offshore wind turbines are installed using the power curve of a reference model of large offshore wind turbines in the 15–20 MW class. As a results, five points at which a capacity factor of over 35% is predicted as promising sites for future wind farm development. These public data from the Korea Meteorological Administration are also limited in their ability to provide accurate wind resource analysis for wind power generation due to the wind speed measurement instrument's height being lower than that of the wind turbine hub and environmental influences. However, these results can be useful in identifying possible candidate sites for wind farms in South Korea.

Low impact siting for wind power facilities in the Southeast United States

AbstractAlthough installed wind power generation capacity in the United States reached 132 GW in 2021, more than quadruple the capacity in 2008, a noticeable void exists in the Southeast. Scant wind power development in this region is due to relatively poorer wind resources, other competitive energy sources, and political opposition. However, the dramatic increases in wind turbine hub height, which allow harvesting the faster wind speeds that occur farther from the ground, combined with a growing sense of urgency to develop renewable energy, point to a near future with significant wind development everywhere, including the Southeast. Nevertheless, the enthusiasm for replacing fossil fuels with renewable sources is tempered by fears that the vast land requirements of utility‐scale wind farms may disrupt valuable ecosystems. In this paper, we identify the areas where installed wind power capacity is least likely to disrupt wildlife and sensitive natural areas in the southeastern United States. The generated maps exclude geographic areas unsuitable for wind power development due to environmental concerns or technical considerations corresponding to five categories. The resulting geospatial product suggests that even after removing sizable areas from consideration, there is significant land for wind development to meet the Southeast's energy needs and clean energy goals.

Wind farm layout and hub height optimization with a novel wake model

This paper comprehensively investigates the impact of wind turbine layout and hub height on power generation of wind farm. Firstly, an engineering three-dimensional (3-D) wind turbine wake model is improved by the artificial neural network (ANN) technology. The novel 3-D ANN wake model reaches more than 99% accuracy of the original wake model. It can save about 80% of the computational time when predicting the downstream wind speed. Secondly, the influence of wind turbine hub height and position on the equivalent wind speed (EWS) and power is deeply studied. Specially, when reducing the hub height of the downstream wind turbine, both the wake impact from upstream turbines and EWS will decrease, so the overall influence should be assessed according to the specific situation. Finally, the problem of wind farm layout and height optimization is investigated. According to this study, simultaneously optimizing these two factors can obtain a better result than optimizing each factor individually. If economic factor is additionally considered, the optimized hub height and power output results will be quite different. Therefore, considering more factors is important to obtain an appropriate wind farm layout.

Integration of atmospheric stability in wind resource assessment through multi-scale coupling method

The abundance level of wind resources is the first and most important factor to consider in the wind farm design. The accuracy of wind energy resource assessment directly determines the operating income of a wind farm after its construction is completed. Therefore, aiming to meet the requirements of accurate wind resource assessment in areas with a complex terrain or those lacking the observation information, this paper proposes a multi-scale coupling numerical simulation method based on atmospheric stability. First, the atmospheric flow field simulation results at the heights of 200 m, 300 m, and 400 m are extracted from the mesoscale Weather Research and Forecasting (WRF) model and used as inlet boundary conditions of a microscale Computational fluid dynamics (CFD) model. Then, to obtain more accurate coupling data at the height of a wind turbine hub, the atmospheric stability parameter is introduced into the multi-scale coupling process, data correction is conducted at the three heights, and a comparison with the observation data of a wind tower is made. Finally, a numerical analysis is conducted using real data of a wind farm in eastern Inner Mongolia and Jilin. The analysis results show that the proposed method can improve the assessing accuracy of wind resources of wind farms using mesoscale data, which is valuable to a certain extent in real-world applications.

Virtual sensing of wind turbine hub loads and drivetrain fatigue damage

This paper presents a Digital Twin for virtual sensing of wind turbine aerodynamic hub loads, as well as monitoring the accumulated fatigue damage and remaining useful life in drivetrain bearings based on measurements of the Supervisory Control and Data Acquisition (SCADA) and the drivetrain condition monitoring system (CMS). The aerodynamic load estimation is realized with data-driven regression models, while the estimation of local bearing loads and damage is conducted with physics-based, analytical models. Field measurements of the DOE 1.5 research turbine are used for model training and validation. The results show low errors of 6.4% and 1.1% in the predicted damage at the main and the generator side high-speed bearing respectively.

Evaluation of best value of wind speed for maximum wind energy output in Nigeria using neural network

The outcome of the study, evaluation of best value of wind speed for maximum wind energy output in Nigeria using neural network was successfully achieved. Wind speed for various months and elevation data gotten from NASA were prepared in excel and imported into neural network toolbox in MATLAB for training and analysis using levenberg maquardt and scaled conjugate gradient algorithms to determine the best performance value. Furthermore, training of the data using levenberg marquardt algorithm at 70% training data, 15% test data and 15% validation data respectively revealed that the best performance level was 1.9046 m/s at 7 epochs and this matches elevation of 7. 24meters. In addition, training of the same data using scaled conjugate gradient algorithm with the stated conditions above, revealed that the best performance level was 9.0376 m/s at 182 epochs and this matches elevation of 34.35meters. Also, training fit coefficients were found to be 0.99984 and 0.99804 respectively which indicated that there is a close and positive relationship between wind speed and wind turbine hub height. Results revealed that the best value of wind speed and elevation for maximum wind energy output in Nigeria ranges from 1.9046 m/s to 9.0376 m/s and 7.2 meters to 34.35 meters respectively. The researchers made the following recommendations: Wind turbine tower and blades should be designed to have wind impact speed capacity beyond the estimated values; further research can also be done in future to establish a more accurate mathematical model between wind speed and wind energy output with other advanced program for generalization, etc.

Applicability of WorldCover in Wind Power Engineering: Application Research of Coupled Wake Model Based on Practical Project

This paper discusses how the incorporation of high-resolution ground coverage dataset ESA WorldCover into a wind flow field and wake simulation calculation, as well as the use of the coupled wake model for wind farm output simulation, can improve the accuracy of wind resource assessment using engineering examples. In the actual case of grid-connected wind farms in central China, SCADA wind speed data is reconstructed to the free flow wind speed in front of the wind turbine impeller using the transfer function of the nacelle, and the wind farm is modeled using OpenWind software, simulating the wind speed at the height of each wind turbine hub and each wind turbine output. The results show that when other initial data are consistent, using ESA’s high-precision land cover dataset WorldCover 10 m to make roughness lengths which improves the wind farm output simulation accuracy by 8.91%, showing that it is worth trying to apply WorldCover 10 m to the wind farm simulation design. At the same time, this case is used to compare and analyze the application of the Eddy-Viscosity wake model and the two coupled wake models based on the Eddy-Viscosity wake model. The results show that the coupled wake model will have higher accuracy than the Deep Array Eddy Viscosity wake model and it is 1.24% more accurate than the Eddy Viscosity wake model, and the ASM Eddy Viscosity wake model is 5.21% more accurate than the Eddy Viscosity wake model.

Projecting Future Energy Production from Operating Wind Farms in North America. Part I: Dynamical Downscaling

Abstract New simulations at 12-km grid spacing with the Weather and Research Forecasting (WRF) Model nested in the MPI Earth System Model (ESM) are used to quantify possible changes in wind power generation potential as a result of global warming. Annual capacity factors (CF; measures of electrical power production) computed by applying a power curve to hourly wind speeds at wind turbine hub height from this simulation are also used to illustrate the pitfalls in seeking to infer changes in wind power generation directly from low-spatial-resolution and time-averaged ESM output. WRF-derived CF are evaluated using observed daily CF from operating wind farms. The spatial correlation coefficient between modeled and observed mean CF is 0.65, and the root-mean-square error is 5.4 percentage points. Output from the MPI-WRF Model chain also captures some of the seasonal variability and the probability distribution of daily CF at operating wind farms. Projections of mean annual CF (CFA) indicate no change to 2050 in the southern Great Plains and Northeast. Interannual variability of CFA increases in the Midwest, and CFA declines by up to 2 percentage points in the northern Great Plains. The probability of wind droughts (extended periods with anomalously low production) and wind bonus periods (high production) remains unchanged over most of the eastern United States. The probability of wind bonus periods exhibits some evidence of higher values over the Midwest in the 2040s, whereas the converse is true over the northern Great Plains. Significance Statement Wind energy is playing an increasingly important role in low-carbon-emission electricity generation. It is a “weather dependent” renewable energy source, and thus changes in the global atmosphere may cause changes in regional wind power production (PP) potential. We use PP data from operating wind farms to demonstrate that regional simulations exhibit skill in capturing actual power production. Projections to the middle of this century indicate that over most of North America east of the Rocky Mountains annual expected PP is largely unchanged, as is the probability of extended periods of anomalously high or low production. Any small declines in annual PP are of much smaller magnitude than changes due to technological innovation over the last two decades.