Wind Turbine Hub Research Articles

Numerical Investigation of Terrain-Induced Turbulence in Complex Terrain by Large-Eddy Simulation (LES) Technique

In the present study, field observation wind data from the time of the wind turbine blade damage accident on Shiratakiyama Wind Farm were analyzed in detail. In parallel, high-resolution large-eddy simulation (LES) turbulence simulations were performed in order to examine the model’s ability to numerically reproduce terrain-induced turbulence (turbulence intensity) under strong wind conditions (8.0–9.0 m/s at wind turbine hub height). Since the wind velocity and time acquired from the numerical simulation are dimensionless, they are converted to full scale. As a consequence, both the standard deviation of the horizontal wind speed (m/s) and turbulence intensity evaluated from the field observation and simulated wind data are successfully in close agreement. To investigate the cause of the wind turbine blade damage accident on Shiratakiyama Wind Farm, a power spectral analysis was performed on the fluctuating components of the observed time series data of wind speed (1 s average values) for a 10 min period (total of 600 data) by using a fast Fourier transform (FFT). It was suggested that the terrain-induced turbulence which caused the wind turbine blade damage accident on Shiratakiyama Wind Farm was attributable to rapid wind speed and direction fluctuations which were caused by vortex shedding from Tenjogadake (elevation: 691.1 m) located upstream of the wind farm.

Interannual variability of wind climates and wind turbine annual energy production

Abstract. The interannual variability (IAV) of expected annual energy production (AEP) from proposed wind farms plays a key role in dictating project financing. IAV in preconstruction projected AEP and the difference in 50th and 90th percentile (P50 and P90) AEP derive in part from variability in wind climates. However, the magnitude of IAV in wind speeds at or close to wind turbine hub heights is poorly defined and may be overestimated by assuming annual mean wind speeds are Gaussian distributed with a standard deviation (σ) of 6 %, as is widely applied within the wind energy industry. There is a need for improved understanding of the long-term wind resource and the IAV therein in order to generate more robust predictions of the financial value of a wind energy project. Long-term simulations of wind speeds near typical wind turbine hub heights over the eastern USA indicate median gross capacity factors (computed using 10 min wind speeds close to wind turbine hub heights and the power curve of the most common wind turbine deployed in the region) that are in good agreement with values derived from operational wind farms. The IAV of annual mean wind speeds at or near typical wind turbine hub heights in these simulations and AEP computed using the power curve of the most commonly deployed wind turbine is lower than is implied by assuming σ=6 %. Indeed, rather than 9 out of 10 years exhibiting AEP within 0.9 and 1.1 times the long-term mean AEP as implied by assuming a Gaussian distribution with σ of 6 %, the results presented herein indicate that in over 90 % of the area in the eastern USA that currently has operating wind turbines, simulated AEP lies within 0.94 and 1.06 of the long-term average. Further, the IAV of estimated AEP is not substantially larger than IAV in mean wind speeds. These results indicate it may be appropriate to reduce the IAV applied to preconstruction AEP estimates to account for variability in wind climates, which would decrease the cost of capital for wind farm developments.

On the inter-annual variability of wind energy generation – A case study from Germany

The intermittent and stochastic nature of the wind resource complicates constant electricity supply in countries with high wind energy share in the electricity mix. Therefore, the goal of this study was to quantify the inter-annual variability of wind energy generation on the national scale by estimating upper and lower limits of annual wind energy generation (WEG). A novel methodology was developed and is presented for Germany, where onshore wind energy already accounts for more than 15% of net electricity consumption. First, a comprehensive wind turbine data set was produced including all onshore wind turbines operating in 2017. Next, the wind speed-wind shear model (WSWS) was used to reconstruct the high spatial resolution (200 m × 200 m) annual wind speed distributions in the wind turbine hub height range 30–179 m above ground level in the period 1979–2017. By using wind turbine-specific power curves, the annual wind energy yield was calculated for each wind turbine. It was summed up for the entire country, yielding WEG. Then, 16 theoretical distributions were fitted to WEG. From the fitted distributions, long-term return values of WEG were calculated. In a 100-year period (probability 98%), WEG lies between 67 and 112 TWh/yr and the annual greenhouse gas mitigation potential varies between 45.6 and 76.3 Mio. tCO2-equiv. under current climate. The great WEG-range emphasizes the importance of considering upper and lower WEG-limits for ensuring constant electricity supply at the national scale.

Combined optimization of continuous wind turbine placement and variable hub height

This paper aims to systematically study the wind farm optimization with the continuous selection of wind turbine placement (using Cartesian coordinates) and wind turbine hub height (restrained among a predefined range). Two case studies are performed. The first case involves an ideal two-dimensional (i.e., flat terrain) wind farm, and the other is a three-dimensional wind farm with real terrain altitudes. The schemes of simultaneously optimizing the wind farm layout and wind turbine hub heights applying the simplified and augmented PARK wake model are established for the ideal and real wind farm cases, respectively. The results show that applying different wind turbine hub heights for the wind farm layout optimization yields significant improvement: up to a 0.2 MW increase in total power and a 2% increase of the wind farm efficiency for the ideal wind farm. However, for the real wind farm the effectiveness of applying different wind turbine hub heights varies depending on the number of turbines installed. With less number of turbines installed, the impact of varying hub heights is small. However, significant improvements can be achieved as the number of turbines increases. With 39 wind turbines, the wind farm cost of energy can be reduced by $15000 per megawatt and the wind farm efficiency can increase by up to 0.2%. Given the nameplate capacity and the lifespan of wind farm project, the resulting effect on total energy production can be significant and thus improve the competitiveness of wind power exploitation.

Morphological Optimization of Graphite in Spheroidal Cast Iron with a Modulus of 4 cm used in the Manufacture of Wind Turbine Hubs

Abstract The aim of this study was the morphological optimization of graphite by generating knowledge able to determine which metallurgical manufacturing factors have a significant influence on the geometry of the graphite precipitated in spheroidal grey cast irons with a mass coefficient of 4, used in the manufacture of wind turbine hubs. The research study was conducted on an industrial scale applying a fractional Design of Experiments (DoE) with 7 factors, 16 experiments and resolution IV. The following constitute the most noteworthy novel results obtained in the study. The effect of La on the graphite “counts” is strengthened by pre-inoculation of the molten iron bath with SiC. Furthermore, graphite counts can be increased under low carbon equivalent conditions using post-inoculants containing Zr. Finally, high values of Mn lead to a reduction in the size of the precipitated graphite.

Study on an innovative three-dimensional wind turbine wake model

In this paper, to help handle wind farm optimization problems, an original analytical three-dimensional wind turbine wake model is presented and validated. Compared with existing analytical wake models, the presented wake model also considers the wind variation in the height direction, which is more accurate and closer to the reality. The wake model is based on the flow flux conservation law, and it assumes that the wind deficit downstream of a wind turbine is Gaussian-shaped. The derivation process is described in detail. The wake model is validated by published wind tunnel measurement data at both horizontal and vertical sections. Detailed relative errors are analyzed: the relative errors are mostly within 5% in the horizontal profile validation and within 3% in the vertical profile validation. Based on the wake model, a series of prediction results from multiple views and at different positions are demonstrated. From the predictions, the three-dimensional wake model is effective in describing the spatial distribution of wind velocity. It makes a theoretical contribution to the single wake study, and it is also meaningful to the further study of multiple wakes. Because height is considered in the three-dimensional wake model, the model can be used to optimize wind turbine hub heights and to solve more wind farm layout optimization problems, which will further contribute to increasing the energy output and decreasing the cost of energy.

Optimización microestructural de fundiciones grises dúctiles no aleadas, con matriz ferrítica, empleadas en la fabricación de bujes de aerogeneradores

El objetivo de este trabajo fue la optimización microestructural de fundiciones grises con grafito esferoidal y matriz ferrítica, empleadas en la fabricación de bujes de aerogeneradores, prestando una especial atención a la geometría y distribución de los esferoides de grafito para asegurar el cumplimiento de las propiedades mecánicas requeridas para esta aplicación. En un entorno de gran competitividad y exigencia, marcado por los estándares internacionales que establecen las propiedades mecánicas mínimas que deben cumplir los materiales empleados, el cumplimiento de este objetivo se llevó a cabo correlacionando microestructura y propiedades. Para ello, la metodología de investigación consistió en la generación de conocimiento a partir de coladas industriales “a medida”, seguido de un análisis de la microestructura, con el fin de extraer conclusiones importantes, de aplicación práctica en el proceso de fabricación, mediante el empleo de técnicas estadísticas. La herramienta estadística empleada fue un Diseño de Experimentos Fraccional (DOE), con 7 factores, 16 experimentos y resolución IV. Las muestras empleadas en cada experimento fueron cubos de igual geometría, y con un módulo de masividad de valor 4 cm, el cual se encuentra entre los valores más altos alcanzados en los centros reales de los bujes de aerogeneradores con potencia de 3 MW. Se concluye que el empleo de nodulizantes con trazas de lantano favorece la reducción de la fracción de volumen de perlita, aunque se ha demostrado que la presencia de lantano resulta negativa para promover la morfología esferoidal del grafito primario. A su vez, se ha constatado, sobre la morfología esferoidal del grafito, el efecto negativo del empleo de preinoculantes que contienen SiC, y también que la utilización de chatarra con bajo contenido de Mn favorece la formación de grafito y la reducción de la fracción en volumen de perlita. Se ha comprobado que el efecto “blanqueante” del Mn se ve minimizado con bajos carbonos equivalentes.

Optimal design of a Hydrogen Refuelling Station (HRFS) powered by Hybrid Power System

The use of hydrogen as transportation fuel is considered to be a favourable alternative to fossil fuels. It is believed that the development of fuel cell vehicles will greatly facilitate reduction of greenhouse gas emissions from the transportation sector due to the fact that these vehicles are fuelled by hydrogen, which can be produced by a wide range of processes using renewable energy sources. In order to promote the use of fuel cell vehicles, it is imperative to build the necessary facilities to support this need in the near future, including hydrogen refuelling stations powered by renewable power generation systems. In this study, techno-economic analysis was performed for a hydrogen refuelling station powered by two types of hybrid renewable power generation systems (wind-photovoltaic-battery and wind-battery systems) which will be installed on the island of Gökçeada, Turkey. The analysis was carried out to assess the feasibility of the hydrogen refuelling station to refuel 25 vehicles on a daily basis throughout the year, using HOMER software. Based on the results, the levelized cost of hydrogen for the hydrogen refuelling station powered by the hybrid wind-photovoltaic-battery and wind-battery systems is $ 8.92/kg and $ 11.08/kg, respectively. The levelized cost of hydrogen was also determined for different variable parameters (wind speed, wind turbine hub height, solar irradiance, and project lifetime). It is concluded that the hydrogen refuelling station powered by the proposed renewable power generation systems may be feasible for the chosen site.

Use of spatio-temporal calibrated wind shear model to improve accuracy of wind resource assessment

Wind shear models are commonly used to predict the wind speed at wind turbine hub heights from the wind data collected at the elevation of the monitoring station. In many cases, the model parameters are based on empirical values recommended by design specifications that reflect the site conditions. This paper evaluates the benefits of incorporating site-specific wind data to calibrate the wind shear model parameters. Wind speed data collected by a ZephIR® Light Detection and Ranging (LiDAR) system over a 2-year (Oct. 2010–Sept. 2012) period are used for the analyses. The atmospheric stability is found to have appreciable effects on the wind shear parameters, i.e. wind shear coefficients (WSC) for the power law model and roughness lengths for the logarithmic law wind shear models. The calibrated wind shear model parameters by the monitored wind data during the first year are presented in the format of a contour map to demonstrate the spatio-temporal variations, which shows daily and seasonal variations. The calibrated wind shear models are then validated by the wind data collected during the second year, which demonstrates decent performance. The accuracy and performance of incorporating site-specific wind shear model calibration to predict the wind energy resource is evaluated, where six different methods are compared. The results show that the consideration of spatio-temporal variations of wind shear model parameters achieved improve performance over the application of the empirical or yearly-averaged wind shear model parameters in extrapolating the wind speed. It is also found that the performance of considering spatio-temporal wind shear parameters are even better at higher elevations. Furthermore, the analyses find that the use of empirical wind shear model parameters underestimates the wind energy output at the studied sites. Site-specific calibration of the wind shear models could further improve the accuracy of wind energy assessment by considering the site condition and the variability in the atmospheric stability.

Assessing Global Ocean Wind Energy Resources Using Multiple Satellite Data

Wind energy, as a vital renewable energy source, also plays a significant role in reducing carbon emissions and mitigating climate change. It is therefore of utmost necessity to evaluate ocean wind energy resources for electricity generation and environmental management. Ocean wind distribution around the globe can be obtained from satellite observations to compensate for limited in situ measurements. However, previous studies have largely ignored uncertainties in ocean wind energy resources assessment with multiple satellite data. It is against this background that the current study compares mean wind speeds (MWS) and wind power densities (WPD) retrieved from scatterometers (QuikSCAT, ASCAT) and radiometers (WindSAT) and their different combinations with National Data Buoy Center (NDBC) buoy measurements at heights of 10 m and 100 m (wind turbine hub height) above sea level. Our results show an improvement in the accuracy of wind resources estimation with the use of multiple satellite observations. This has implications for the acquisition of reliable data on ocean wind energy in support of management policies.

Wind Turbine Blade Fault Detection Using Wavelet Power Spectrum and Experimental Modal Analysis

The dynamic behavior of modern multi-Megawatt wind turbines has become an important design consideration. The power generated by wind turbine depends mainly on the interaction between the blades and the flowing wind. Therefore, the major aspects related to the operational reliability of a wind turbine are the aerodynamics characteristics and integrity of the blades. This paper demonstrates the results of the application of wavelet transform combined with experimental modal analysis as a tool for wind turbine blade condition monitoring. An experimental model analysis with different vibrational excitations performed for healthy and cracked cantilever stepped beam, which approximate the wind turbine blade when attached to the wind turbine hub. The beam response signals acquired for three different locations of vibration sensor. The cantilever beam with breathing crack response exhibits a non-stationary vibrational signal because of crack open-close cycles during the oscillation of the beam. The wavelet transform as a time-frequency signal processing technique is an effective tool to extract the characteristics of the non-stationary signals; therefore, used in this paper to analyze the cracked beam vibrational signals. The wavelet power spectrum (WPS) calculated for different beam health conditions and for different locations of the vibration sensors. The results reveal the effectiveness of the wavelet power spectrum over the traditional FFT spectrum to identify the existence of the crack in the wind turbine blade. Moreover, a mathematical model, which correlates the variation of the blade natural frequencies with the crack severity and location, is presented and validated with an experimental modal analysis data.

Sensitivity Analysis of the WRF Model: Wind-Resource Assessment for Complex Terrain

AbstractWind energy requires accurate forecasts for adequate integration into the electric grid system. In addition, global atmospheric models are not able to simulate local winds in complex terrain, where wind farms are sometimes placed. For this reason, the use of mesoscale models is vital for estimating wind speed at wind turbine hub height. In this regard, the Weather Research and Forecasting (WRF) Model allows a user to apply different initial and boundary conditions as well as physical parameterizations. In this research, a sensitivity analysis of several physical schemes and initial and boundary conditions was performed for the Alaiz mountain range in the northern Iberian Peninsula, where several wind farms are located. Model performance was evaluated under various atmospheric stabilities and wind speeds. For validation purposes, a mast with anemometers installed at 40, 78, 90, and 118 m above ground level was used. The results indicate that performance of the Global Forecast System analysis and European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim) as initial and boundary conditions was similar, although each performed better under certain meteorological conditions. With regard to physical schemes, there is no single combination of parameterizations that performs best during all weather conditions. Nevertheless, some combinations have been identified as inefficient, and therefore their use is discouraged. As a result, the validation of an ensemble prediction system composed of the best 12 deterministic simulations shows the most accurate results, obtaining relative errors in wind speed forecasts that are <15%.

Short-term wind power forecasting using a double-stage hierarchical ANFIS approach for energy management in microgrids

Determination of the output power of wind generators is always associated with some uncertainties due to wind speed and other weather parameters variation, and accurate short-term forecasts are essential for their efficient operation. This can efficiently support transmission and distribution system operators and schedulers to improve the power network control and management. In this paper, we propose a double stage hierarchical adaptive neuro-fuzzy inference system (double-stage hybrid ANFIS) for short-term wind power prediction of a microgrid wind farm in Beijing, China. The approach has two hierarchical stages. The first ANFIS stage employs numerical weather prediction (NWP) meteorological parameters to forecast wind speed at the wind farm exact site and turbine hub height. The second stage models the actual wind speed and power relationships. Then, the predicted next day’s wind speed by the first stage is applied to the second stage to forecast next day’s wind power. The influence of input data dependency on prediction accuracy has also been analyzed by dividing the input data into five subsets. The presented approach has resulted in considerable forecasting accuracy enhancements. The accuracy of the proposed approach is compared with other three forecasting approaches and achieved the best accuracy enhancement than all.

Lake Michigan Wind Assessment Analysis, 2012 and 2013

A study was conducted to address the wind energy potential over Lake Michigan to support a commercial wind farm. Lake Michigan is an inland sea in the upper mid-western United States. A laser wind sensor mounted on a floating platform was located at the mid-lake plateau in 2012 and about 10.5 kilometers from the eastern shoreline near Muskegon Michigan in 2013. Range gate heights for the laser wind sensor were centered at 75, 90, 105, 125, 150, and 175 meters. Wind speed and direction were measured once each second and aggregated into 10 minute averages. The two sample t-test and the paired-t method were used to perform the analysis. Average wind speed stopped increasing between 105 m and 150 m depending on location. Thus, the collected data is inconsistent with the idea that average wind speed increases with height. This result implies that measuring wind speed at wind turbine hub height is essential as opposed to using the wind energy power law to project the wind speed from lower heights. Average speed at the mid-lake plateau is no more that 10% greater than at the location near Muskegon. Thus, it may be possible to harvest much of the available wind energy at a lower height and closer to the shoreline than previously thought. At both locations, the predominate wind direction is from the south-southwest. The ability of the laser wind sensor to measure wind speed appears to be affected by a lack of particulate matter at greater heights.Article History: Received June 15th 2016; Received in revised form January 16th 2017; Accepted February 2nd 2017 Available onlineHow to Cite This Article: Standridge, C., Zeitler, D., Clark, A., Spoelma, T., Nordman, E., Boezaart, T.A., Edmonson, J., Howe, G., Meadows, G., Cotel, A. and Marsik, F. (2017) Lake Michigan Wind Assessment Analysis, 2012 and 2013. Int. Journal of Renewable Energy Development, 6(1), 19-27.http://dx.doi.org/10.14710/ijred.6.1.19-27

CFD wind turbines wake assessment in complex topography

The purpose of this work is to numerically study the wind turbine wake evolution in farm over both complex and flat terrain. The nonlinear, aerodynamic interaction between the rotor wake and the wind farm terrain is modeled using the Hybrid method combining CFD (Computational Fluid dynamics) with the actuator disk model. The rotor defined as a function of the wind speed and the thrust coefficient is applied through a source term added in Navier-Stokes equations within (RANS) decomposition. The framework is structured and resolved via an Open Source Computational fluid dynamics program based on the finite volume method. The interactions between atmospheric upwind boundary layer, downwind wake and ground effects are evaluated considering different wind farm configurations: First, simulations are conducted for flat terrain by varying the wind turbine hub height. The soil effects on the wake evolution are estimated by means of the size length of the eddy areas of low speed recorded behind the rotor. In the second configuration concerning the complex terrain, the proposed hybrid method is adapted to the local wind field significantly disturbed by the topography singularities. The flow field obtained at the hub level is then analyzed and used to define the corresponding actuator disk model. This approach is applied to a small region located in north of Algeria. However, the accuracy and performance of the proposed model to predict the near wake and the far wake are demonstrated by a comparison with wake measurement over flat terrain and are in good agreement with experimental data. Results obtained in all cases gives interesting information involved in wind farm layout.

Numerical Prediction and Field Verification Test of Wind-Power Generation Potential in Nearshore Area Using a Moored Floating Platform

The offshore turbine system was installed on a floating platform moored in Hakata Bay, offshore of Fukuoka, Japan. An identical turbine system was also installed at the adjacent waterfront. The separation of the two turbines was 3.7 km. Wind flow tends to be more stable and the average wind speed is often larger in offshore areas than adjacent land areas at typical wind turbine hub height. This study focused on the wind condition of a nearshore area to clarify the advantages of nearshore wind farming. Prior to field experiment, wind conditions were predicted by using numerical simulation. It is useful for estimating topographical effect in nearshore areas. Next, field verification test was done by directly comparing wind data obtained from the identical wind turbine systems installed at an offshore location and the adjacent waterfront over the same extended period. The corresponding power output of these turbines was also compared. The data set exhibits 23% larger annual average wind speed at the offshore location and smaller turbulent intensity, resulting doubled annual power production.

Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast

The forecast of wind energy is closely linked to the prediction of the variation of winds over very short time intervals. Four wind towers located in the Inner Mongolia were selected to understand wind power resources in the compound plateau region. The mesoscale weather research and forecasting combining Yonsei University scheme and Noah land surface model (WRF/YSU/Noah) with 1-km horizontal resolution and 10-min time resolution were used to be as the wind numerical weather prediction (NWP) model. Three statistical techniques, persistence, back-propagation artificial neural network (BP-ANN), and least square support vector machine (LS-SVM) were used to improve the wind speed forecasts at a typical wind turbine hub height (70 m) along with the WRF/YSU/Noah output. The current physical-statistical forecasting techniques exhibit good skill in three different time scales: (1) short-term (day-ahead); (2) immediate-short-term (6-h ahead); and (3) nowcasting (1-h ahead). The forecast method, which combined WRF/YSU/Noah outputs, persistence, and LS-SVM methods, increases the forecast skill by 26.3-49.4% compared to the direct outputs of numerical WRF/YSU/Noah model. Also, this approach captures well the diurnal cycle and seasonal variability of wind speeds, as well as wind direction. Predicción de vientos en una altiplanicie a la altura del eje con el esquema de la Universidad Yonsei/Modelo Superficie Terrestre Noah y la predicción estadísticaResumenLa estimación de la energía eólica está relacionada con la predicción en la variación de los vientos en pequeños intervalos de tiempo. Se seleccionaron cuatro torres eólicas ubicadas al interior de Mongolia para estudiar los recursos eólicos en la complejidad de un altiplano. Se utilizó la investigación climática a mesoscala y la combinación del esquema de la Universidad Yonsei con el Modelo de Superficie Terrestre Noah (WRF/YSU/Noah), con resolución de 1km horizontal y 10 minutos, como el modelo numérico de predicción meteorológica (NWP, del inglés Numerical Weather Prediction). Se utilizaron tres técnicas estadísticas, persistencia, propagación hacia atrás en redes neuronales artificiales y máquina de vectores de soporte-mínimos cuadrados (LS-SVM, del inglés Least Square Support Vector Machine), para mejorar la predicción de la velocidad del viento en una turbina con la altura del eje a 70 metros y se complementó con los resultados del WRF/YSU/Noah. Las técnicas de predicción físico-estadísticas actuales tienen un buen desempeo en tres escalas de tiempo: (1) corto plazo, un día en adelante; (2) mediano plazo, de seis días en adelante; (3) cercano, una hora en adelante. Este método de predicción, que combina los resultados WRF/YSU/Noah con los métodos de persistencia y LS-SVM incrementa la precisión de predicción entre 26,3 y 49,4 por ciento, comparado con los resultados directos del modelo numérico WRF/YSU/Noah. Además, este método diferencia la variabilidad de las estaciones y el ciclo diurno en la velocidad y la dirección del viento.

Optimization of the Mechanical Behaviour Under Tensile Stress of Spheroidal Cast Iron with Ferritic Matrix used in the Manufacture of Wind Turbine Hubs

The aim of this study is the microstructural optimization of the toughness behaviour at sub-zero temperatures and the enhancement of the ductile behaviour under tensile stress of spheroidal graphite cast irons with a ferrite matrix used to manufacture hubs for horizontal axis wind turbines. The approach employed was a fractional design of experiments with 7 factors, 16 experiments and resolution IV. The research was conducted at an industrial scale on cubes with the same mass coefficient as that of the industrial parts to be enhanced. The main conclusion worth highlighting is that the use of nodulizers with traces of lanthanum enhances the toughness of the material at sub-zero temperatures and improves the ductility of these cast irons at the cost of lowering their yield strength. However, this negative effect may be offset by a decrease in the carbon equivalent to eutectic levels.

A Methodology for the Reconstruction of 2D Horizontal Wind Fields of Wind Turbine Wakes Based on Dual-Doppler Lidar Measurements

Dual-Doppler lidar is a powerful remote sensing technique that can accurately measure horizontal wind speeds and enable the reconstruction of two-dimensional wind fields based on measurements from two separate lidars. Previous research has provided a framework of dual-Doppler algorithms for processing both radar and lidar measurements, but their application to wake measurements has not been addressed in detail yet. The objective of this paper is to reconstruct two-dimensional wind fields of wind turbine wakes and assess the performance of dual-Doppler lidar scanning strategies, using the newly developed Multiple-Lidar Wind Field Evaluation Algorithm (MuLiWEA). This processes non-synchronous dual-Doppler lidar measurements and solves the horizontal wind field with a set of linear equations, also considering the mass continuity equation. MuLiWEA was applied on simulated measurements of a simulated wind turbine wake, with two typical dual-Doppler lidar measurement scenarios. The results showed inaccuracies caused by the inhomogeneous spatial distribution of the measurements in all directions, related to the ground-based scanning of a wind field at wind turbine hub height. Additionally, MuLiWEA was applied on a real dual-Doppler lidar measurement scenario in the German offshore wind farm “alpha ventus”. It was concluded that the performance of both simulated and real lidar measurement scenarios in combination with MuLiWEA is promising. Although the accuracy of the reconstructed wind fields is compromised by the practical limitations of an offshore dual-Doppler lidar measurement setup, the performance shows sufficient accuracy to serve as a basis for 10 min average steady wake model validation.

Numerical simulation of the interaction between tandem wind turbines

Two model wind turbines operating in tandem are simulated using Tenasi, a node-centered, finite volume unstructured flow solver. The turbine blades are designed using the NREL S826 airfoils. The entire test section of the wind tunnel is simulated since the blockage (based on swept area of the rotor and tower area) was 12%. The simulations included the tunnel walls, wind turbine blades, hubs, nacelles and towers. Detailed experimental data is available for a variety of flow conditions (varying tip-speed-ratios). The results presented here are for tip-speed-ratios of 2.5, 4, and 7 for the rear turbine while the front turbine was always operated at the design tip speed ratio of 6. The tunnel wind speed was 10m/s and the wind turbine RPM was varied to achieve the desired tip-speed-ratios. A DES version of the Menter's SST turbulence model is utilized for the turbulence closure. Turbine performance as well as wake data at various locations is compared to experiment (Blind Test 2 study carried out at NTNU, Norway). Very good agreement is observed for the turbine performance. Good agreement was obtained for velocity fluctuations in the wake region with trends captured very well. Mean velocity predictions agree reasonably well.