Wind Turbine Hub Height Research Articles

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.

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.

Wind energy evaluation for a highly complex terrain using Computational Fluid Dynamics (CFD)

Computational Fluid Dynamics (CFD) modeling is becoming an important tool in the wind industry to study wind flow patterns. Accurate CFD simulations of wind flow are essential for the selection of wind farm locations as well as the design of appropriate wind turbines. This article validates the average wind power estimated by the state of the art CFD tool WindSim using on-site measurements from nine meteorological stations scattered around a highly complex terrain at several heights. It is known that the numerical solver is very sensitive to the wide number of computational parameters that have to be taken into consideration by the user. This paper investigates those computational parameters in details including a grid dependency test, the order of the discretization schemes, the turbulence models (Standard k-ε, k-ε with Yap corrections, RNG k-ε and Modified k-ε) and the iterative convergence criteria. The best model is employed to investigate major hot spots identified where wind farming is feasible in Mauritius with due consideration to land use and topographical requirements. Wind maps are produced at four levels which are of typical hub heights of commercial wind turbines. These maps can be used to assist in the decision-making process when locating best placements for wind farming.

Prediction of local wind climatology from Met Office models: Virtual Met Mast techniques

AbstractThe Met Office has developed the Virtual Met Mast™ (VMM) tool for assessing the feasibility of potential wind farm sites. It provides site‐specific climatological wind information for both onshore and offshore locations. The VMM relies on existing data from past forecasts from regional‐scale numerical weather prediction (NWP) models, to which corrections are applied to account for local site complexity. The techniques include corrections to account for the enhanced roughness lengths used in NWP models to represent drag due to sub‐grid orography and downscaling methods that predict local wind acceleration over small‐scale terrain. The corrected NWP data are extended to cover long periods (decades) using a technique in which the data are related to alternative long‐term datasets. For locations in the UK, the VMM currently relies on operational mesoscale model forecast data at 4 km horizontal resolution. Predictions have been verified against observations made at typical wind turbine hub heights at over 80 sites across the UK. In general, the predictions compare well with the observations. The techniques provide an efficient method for screening potential wind resource sites. Examples of how the VMM techniques can be used to produce local wind maps are also presented. © 2016 Crown copyright. Wind Energy © 2016 John Wiley & Sons, Ltd

Comparison of the effectiveness of analytical wake models for wind farm with constant and variable hub heights

Extensive power losses of wind farm have been witnessed due to the wake interactions between wind turbines. By applying analytical wake models which describe the wind speed deficits in the wake quantitatively, the power losses can be regained to a large extent through wind farm layout optimization, and this has been extensively reported in literature. Nevertheless, the effectiveness of the analytical wake models in predicting the wind farm power production have rarely been studied and compared for wind farm with both constant and variable wind turbine hub heights. In this study, the effectiveness of three different analytical wake models (PARK model, Larsen model and B-P model) is thoroughly compared over a wide range of wake properties. After the validation with the observation data from offshore wind farm, CFD simulations are used to verify the effectiveness of the analytical wake models for an onshore wind farm. The results show that when using the PARK model the surface roughness value (z0) must be carefully tuned to achieve good performance in predicting the wind farm power production. For the other two analytical wake models, their effectiveness varies depending on the situation of wind farm (offshore or onshore) and the wind turbine hub heights (constant or variable). It was found that the results of B-P model agree well with the CFD simulations for offshore wind farm, but not for the onshore wind farm. The Larsen model is more accurate for the wind farm with variable wind turbine hub heights than those with constant wind turbine hub height.

Wind turbine layout optimization with multiple hub height wind turbines using greedy algorithm

Wind turbine layout optimization in wind farm is one of the most important technologies to increase the wind power utilization. This paper studies the wind turbine layout optimization with multiple hub heights wind turbines using greedy algorithm. The linear wake model and the particle wake model are used for wake flow calculation over flat terrain and complex terrain, respectively. Three-dimensional greedy algorithm is developed to optimize wind turbine layout with multiple hub heights for minimizing cost per unit power output. The numerical cases over flat terrain and complex terrain are used to validate the effectiveness of the proposed greedy algorithm for the optimization problem. The results reveal that it incurs lower computational costs to obtain better optimized results using the proposed greedy algorithm than the one using genetic algorithm. Compared to the layout with identical hub height wind turbines, the one with multiple hub height wind turbines can increase the total power output and decrease the cost per unit power output remarkably, especially for the wind farm over complex terrain. It is suggested that three-dimensional greedy algorithm is an effective method for more benefits of using wind turbines with multiple hub heights in wind farm design.

Hurricane risk assessment for offshore wind plants

This study describes a framework for estimation of structural damage to offshore wind plants during hurricanes. Related risk assessment is fundamentally dependent on the estimation of hurricane-generated wind speed exceedance probabilities at selected hub heights of wind turbines in the plant and on estimation of associated wind turbine loads. As part of a framework for risk assessment introduced here, synthetic storm tracks are first simulated over the ocean using available historical tropical storm data; then, a hurricane intensity evolution model based on thermodynamic and atmospheric environmental variables is developed for each simulated track as it approaches the chosen wind plant site. Based on this intensity model, a turbulent wind field can be simulated at any location of interest along the hurricane track. The simulated turbulent wind field can then be used to estimate wind speed exceedance probability distributions and, when combined with partially correlated waves, it can also be used in analyzing the response of individual turbines in a wind plant. A framework for the overall risk assessment is presented; individual components that are part of such a framework are described briefly in illustrative applications. Finally, a brief discussion is presented that addresses issues related to the concept of “robustness” checks that are often considered in the context of safe performance-based design. This concept employed in the oil and gas industries provides for a secondary margin for “beyond design-level” external conditions out to more extreme conditions as might accompany hurricanes.

Observations of offshore wind characteristics by Doppler-LiDAR for wind energy applications

The interest of investigating offshore wind characteristics is receiving growing attention worldwide, which mainly attempts to boost the development of offshore wind energy and accelerate sustainable development. In particular, the understanding of offshore wind characteristics at the hub heights of wind turbines is of great significance in regard to the assessment of economic feasibility and structural design of wind turbines. Since traditional meteorological masts have limitations in height due to economic and technical considerations, remote sensing techniques have increasingly been employed for offshore wind observations.This paper presents a comprehensive assessment of offshore wind characteristics on the basis of measurements from a light detection and ranging (LiDAR) system at an offshore site in Hong Kong. The fidelity of the wind LiDAR measurements was firstly examined by comparing with those obtained from anemometers on a nearby meteorological mast. Statistical analysis of wind speed was performed by means of the Weibull distribution function, and the results were subsequently used to facilitate the determination of several characteristic parameters associated with wind energy applications. The vertical profiles of wind speed derived from the LiDAR system were compared with existing models. Characteristics of wind shear coefficient were examined within different height ranges. Furthermore, the offshore turbulence characteristics were investigated, including the turbulence intensity and gust factor. The outcomes of this study are expected to provide useful information for the development of offshore wind energy.

Optimization of a hybrid tower for onshore wind turbines by Building Information Modeling and prefabrication techniques

Abstract Background Nowadays wind energy is becoming increasingly significant in the planning, development and growth of new electricity supply systems. Special attention has been given to land-based turbines for ensuring the efficient economical operation of massive hubs rising 100m above the ground, based on the idea that the bigger the turbine, the more complicated are the transportation and assembly processes. Methods A new design of a wind turbine has several advantages compared to conventional designs; one of these advantages lies in the use of prefabricated elements, which increases efficiency. The implementation of information technology as a complement to prefabrication techniques is a further aim of this research, which seeks to improve the overall performance of the project. Consequently, Building Information Modelling is suggested as the most suitable methodology for complementing off-site techniques and reaching higher efficiency by improving design, manufacture, transportation and assembly processes. Results This paper will present the research project “hybrid² tower for wind turbines” funded by the State of Hesse, Germany, which focuses on a new, efficient and economical design for high wind turbine towers. The new hybrid² tower is composed of a concrete tower containing prefabricated concrete quarter-circle elements, steel beams and a steel tube tower on the top. The combination of concrete and steel beams improves the static and dynamic performance of the main supporting structure. With this new design, the weight of the concrete tower is estimated to decrease by 40 % compared to a traditional full-concrete tower and, as a positive consequence, the cost of assembly (including assembly on site) is reduced. Conclusions Due to the energy revolution, a special focus is put on the development of renewable energies, especially wind power. The steadily increasing hub heights of wind turbines means that tower structures have to be more massive. The development of the hybrid² tower by using Building Information Modeling and prefabrication techniques leads to an optimized performance and reduces transport and assembly costs.

Complex terrain effects on wake characteristics of a parked wind turbine

In order to extend lifetime and enhance energy production of wind turbines in complex terrain it is important to learn about their wake characteristics. Hence, wind-tunnel experiments are carried out to analyze the wind-turbine wake downwind of a mountain. The wind-tunnel simulation of the neutrally stratified atmospheric boundary layer (ABL) developing above a flat terrain is first generated using the well established Counihan approach. Once a well developed ABL simulation is achieved for a flat terrain, three various mountain models are separately exposed to that ABL simulation, as well as the flat terrain as a reference case. Wake characteristics of a single wind-turbine model in the wake of those four various terrain models are then studied. The ratios between the height of different mountains to the wind-turbine hub height are 0, 0.417, 0.833, 0.833 (0.417) for the flat terrain, small mountain, large mountain, mountain with a bay, respectively. For the mountain with a bay, there are two different ratios between the mountain height and the wind-turbine hub height; 0.833 at the lateral edges of the mountain, 0.417 in the lateral center of the mountain. All three mountain models are 600mm long in the main wind direction and 1000mm wide laterally to the main wind direction. Small mountain model is 100mm uniformly high, large mountain model is 200mm uniformly high. The mountain with a bay model is 200mm high with a normal slope to 100mm height in the lateral center of the mountain model, whereas this cavity is 200mm wide in the lateral direction. The calculated ABL simulation length scale factor is 1:300, and it is applied on mountain models and the wind-turbine model as well. The wind-turbine model is designed to correspond to commonly used prototype wind turbines. The experiments are carried out for the wind-turbine model in parking position to analyze trends expected in a strong wind situation, when there is no rotation of the rotor blades. Wake characteristics analyzed with respect to the mean wind velocity, turbulence intensity and velocity power spectra indicate several important findings. In particular, the observed flow retardation is more exhibited near the ground surface and the mountains. The mountain-induced flow disturbance enhances with increasing the size and complexity of the mountains. Turbulence intensity in the wake of the mountain and wind turbine is considerably larger than in the atmospheric boundary layer. Velocity power spectra are strongly influenced by the terrain complexity, whereas the effects of the mountain and surface roughness are mostly constrained up to the double mountain height. There is a strong energy content at frequencies corresponding to a free shear layer separating from the mountain ridge. Dominant turbulence structures are displaced vertically, as the wake is transported with the flow in the main wind direction.

Multi-objective genetic algorithm based innovative wind farm layout optimization method

Layout optimization has become one of the critical approaches to increase power output and decrease total cost of a wind farm. Previous researches have applied intelligent algorithms to optimizing the wind farm layout. However, those wind conditions used in most of previous research are simplified and not accurate enough to match the real world wind conditions. In this paper, the authors propose an innovative optimization method based on multi-objective genetic algorithm, and test it with real wind condition and commercial wind turbine parameters. Four case studies are conducted to investigate the number of wind turbines needed in the given wind farm. Different cost models are also considered in the case studies. The results clearly demonstrate that the new method is able to optimize the layout of a given wind farm with real commercial data and wind conditions in both regular and irregular shapes, and achieve a better result by selecting different type and hub height wind turbines.

Climatology and trend of wind power resources in China and its surrounding regions: a revisit using Climate Forecast System Reanalysis data

ABSTRACTThe mean climatology, seasonal and interannual variability and trend of wind speeds at the hub height (80 m) of modern wind turbines over China and its surrounding regions are revisited using 33‐year (1979–2011) wind data from the Climate Forecast System Reanalysis (CFSR) that has many improvements including higher spatial resolution over previous global reanalysis products. Mean 80‐m wind speeds are consistently higher over China Seas and the ocean areas than over land, and inside China high winds are found in areas of Inner Mongolia and the Tibetan Plateau. There is a considerable seasonal variability that reflects primarily the influence of East and Southeast Asia Monsoon with generally higher speeds in winter followed by summer, and weaker winds in autumn, followed by spring. There is also a strong interannual variability, and regions of larger amplitude of variability coincide with regions of higher mean winds. A decreasing trend, dominated by a sharp decline beginning in 2005, is seen across China and the surrounding seas in summer and autumn, and the summer trends over land and over ocean appear to be related respectively to the Pacific Decadal Oscillation (PDO) and the East Asian summer monsoon. Trends are not consistent across the region in spring and winter, however, with positive trends over some areas in northeastern and northwestern China, Mongolia and tropical oceans whereas negative trends in other regions. Nearly all areas of China experience mean annual 80‐m wind speed less than 6.9 m s−1 (wind power classes of 1–2) except for some areas of Inner Mongolia where mean annual 80‐m wind speeds exceed 6.9 m s−1 (Classes 3 or higher, suitable for wind energy development). China Seas and ocean areas generally fall in Class 3 or above, with the Taiwan and Luzon Straights reaching the highest Class 7.

Investigation of boundary-layer wind predictions during nocturnal low-level jet events using the Weather Research and Forecasting model

The accuracy of boundary-layer wind profiles occurring during nocturnal low-level jet (LLJ) events, and their sensitivities to variations of user-specifiable model configuration parameters within the Weather Research and Forecasting model, was investigated. Simulations were compared against data from a wind-profiling lidar, deployed to the Northern Great Plains during the U.S. Department of Energy-supported Weather Forecast Improvement Project. Two periods during the autumn of 2011 featuring LLJs of similar magnitudes and durations occurring during several consecutive nights were selected for analysis. Simulated wind speed and direction at 80 and 180 m above the surface, the former a typical wind turbine hub height, bulk vertical gradients between 40 and 120 m, a typical rotor span, and the maximum wind speeds occurring at 80 and 180 m, and their times of occurrence, were compared with the observations. Sensitivities of these parameters to the horizontal and vertical grid spacing, planetary boundary layer and land surface model physics options, and atmospheric forcing dataset, were assessed using ensembles encompassing changes of each of these configuration parameters. Each simulation captured the diurnal cycle of wind speed and stratification, producing LLJs during each overnight period; however, large discrepancies in relation to the observations were frequently observed, with each ensemble producing a wide range of distributions, reflecting highly variable representations of stratification during the weakly stable overnight conditions. Root mean square error and bias values computed over the LLJ cycle (late evening through the following morning) revealed that, while some configurations performed better or worse in different aspects and at different times, none exhibited definitively superior performance. The considerable root mean square error and bias values, even among the ‘best’ performing simulations, underscore the need for improved simulation capabilities for the prediction of near-surface winds during LLJ conditions. Copyright © 2015 John Wiley & Sons, Ltd.

Seasonal and yearly wind speed distribution and wind power density analysis based on Weibull distribution function

Wind energy, which is among the most promising renewable energy resources, is used throughout the world as an alternative to fossil fuels. In the assessment of wind energy for a region, the use of two-parameter Weibull distribution is an important tool. In this study, wind speed data, collected for a one year period between June 2012 and June 2013, were evaluated. Wind speed data, collected for two different heights (20 m and 30 m) from a measurement station installed in Atılım University campus area (Ankara, Turkey), were recorded using a data logger as one minute average values. Yearly average hourly wind speed values for 20 m and 30 m heights were determined as 2.9859 m/s and 3.3216 m/s, respectively. Yearly and seasonal shape (k) and scale (c) parameter of Weibull distribution for wind speed were calculated for each height using five different methods. Additionally, since the hub height of many wind turbines is higher than these measurement heights, Weibull parameters were also calculated for 50 m height. Root mean square error values of Weibull distribution functions for each height, derived using five different methods, show that a satisfactory representation of wind data is achieved for all methods. Yearly and seasonal wind power density values of the region were calculated using the best Weibull parameters for each case. As a conclusion, the highest wind power density value was found to be in winter season while the lowest value was encountered in autumn season. Yearly wind power densities were calculated as 39.955 (W/m2), 51.282 (W/m2) and 72.615 (W/m2) for 20 m, 30 m and 50 m height, respectively. The prevailing wind direction was also determined as southeast for the region. It can be concluded that the wind power density value at the region is considerable and can be exploited using small scale wind turbines.

Assessing the technical wind energy potential in Africa a GIS-based approach

Nowadays, Africa faces the challenge of generating more electricity to meet existing and future demand in a sustainable way. Africa is a privileged continent in terms of wind resource regime. However, very little of this has been utilized. It is thus essential to identify and define the amount of wind energy that could be technically exploited on the continent. The utilization of wind energy is associated with a myriad of localization criteria and thus it should be systematically addressed by spatial assessments to guarantee its harmonization with socio-economic systems, infrastructure and ecosystems. This study focuses on onshore wind power on the African continent and strives to provide estimates of theoretical, geographical and technical potential based on state of the art wind power technology. Maps of wind power potential at 80 m, which is the hub height of a modern wind turbine will be derived via statistical distribution of wind speed data and implementation of wind power curves. Screening criteria regarding the localization of wind farms and related to socio-economic and geographic constraints are outlined and implemented through a thorough GIS analysis. The results of this work are presented and compared with similar approaches and significant conclusions are drawn. Based on the analysis there are some countries that signify high yearly wind energy yield, such as South Africa, Sudan, Algeria, Egypt, Libya, Nigeria, Mauritania, Tunisia and Morocco, whilst Equatorial Guinea, Gabon, Central African Republic, Burundi, Liberia, Benin and Togo indicate the least wind power potential.

The Strict Relationship Between Surface Turbulence Intensity and Wind Shear Coefficient Daily Courses: A Novel Method to Extrapolate Wind Resource to the Turbine Hub Height

Based on power law, a novel method is proposed to extrapolate surface wind speed ( v ) to the wind turbine (WT) hub height, via prediction of the wind shear coefficient (WSC) daily course, by only using the surface turbulence intensity ( I ) daily course. Work’s main outcome is a strict (almost 1:1) relationship between WSC and I daily courses which was found after applying a linear regression analysis. Practical usefulness of this finding for wind energy applications is straightforward, as merely using I values routinely collected at surface heights a WSC predicting model may be used to fairly estimate energy yield at WT hub height. A 2–year (2012–2013) dataset from the meteorological mast of Cabauw (Netherlands) was used, including 10–min records collected at heights of 10, 20, 40, and 80 m. Methods were trained over a 1–year period (2012) and then validated over an independent 1–year period (2013). WT hub heights of 40 and 80 m have been targeted for the extrapolation, being accomplished based on I observations at two surface levels: 10 and 20 m. As a result, good scores were returned by the proposed method over the most challenging height intervals: between 10 and 80 m, a 5% mean bias was achieved in extrapolated v values and at worst a 11.51% in calculated energy yield; between 20 and 80 m, extrapolated v values were biased by 2%, while energy output at worst by 6.62%.

Investigating the possibility of using different hub height wind turbines in a wind farm

Many researchers have focused on the layout design of a wind farm using the computational methods. Most of previous researches focused on relevant large cell size and using same hub height wind turbines. In this paper, the authors investigate the possibility of using different hub height wind turbines in a wind farm. A limited area (2 km × 2 km) with constant wind speed and direction is considered as the potential wind farm area, and a nested genetic algorithm is used as optimisation algorithm. Two different hub height wind turbines are introduced with two different cell sizes. Power output, cost, payback period, and total profit are selected as evaluation criteria when comparing the layouts with same hub height wind turbines with the layouts with different hub height wind turbines. The results demonstrate that it is feasible and possible to use different hub height wind turbines in a wind farm.

Temporal and Spatial Variability of Wind Resources in the United States as Derived from the Climate Forecast System Reanalysis

Abstract This study examines the spatial and temporal variability of wind speed at 80 m above ground (the average hub height of most modern wind turbines) in the contiguous United States using Climate Forecast System Reanalysis (CFSR) data from 1979 to 2011. The mean 80-m wind exhibits strong seasonality and large spatial variability, with higher (lower) wind speeds in the winter (summer), and higher (lower) speeds over much of the Midwest and U.S. Northeast (U.S. West and Southeast). Trends are also variable spatially, with more upward trends in areas of the Great Plains and Intermountain West of the United States and more downward trends elsewhere. The leading EOF mode, which accounts for 20% (summer) to 33% (winter) of the total variance and represents in-phase variations across the United States, responds mainly to the North Atlantic Oscillation (NAO) in summer and El Niño–Southern Oscillation (ENSO) in the other seasons. The dominant variation pattern can be explained by a southerly/southwesterly (westerly) anomaly over the U.S. East (U.S. West) as a result of the anomalous mean sea level pressure (MSLP) pattern. The second EOF mode, which explains about 15% of the total variance and shows a seesaw pattern, is mainly related to the springtime Arctic Oscillation (AO), the summertime recurrent circumglobal teleconnection (CGT), the autumn Pacific decadal oscillation (PDO), and the winter El Niño Modoki. The anomalous jet stream and MSLP patterns associated with these indices are responsible for the wind variation.

Surface turbulence intensity as a predictor of extrapolated wind resource to the turbine hub height

Based on power law (PL), a novel method is proposed to extrapolate surface wind speed to the wind turbine (WT) hub height, via assessment of wind shear coefficient (WSC), by only using surface turbulence intensity, a parameter actually regarded as a merely critical one in wind energy studies. A 2-year (2012–2013) dataset from the meteorological mast of Cabauw (Netherlands) was used, including 10-min records collected at 10, 20, 40, and 80 m. WT hub heights of 40 and 80 m have been targeted for the extrapolation, being accomplished based on turbulence intensity observations at 10 and 20 m. Trained over the year 2012, the method was validated over the year 2013.Good scores were returned both in wind speed and power density extrapolations, with biases within 7 and 8%, respectively. Wind speed extrapolation was better predicted 10–40 m (NRMSE = 0.16, r = 0.95) than 10–80 and 20–80 m (NRMSE = 0.20–0.24, r = 0.86–0.91), while for power density even finer scores than wind speed were achieved (r = 0.98 at 40 m, and r = 0.96 at 80 m). Method's skills were also assessed in predicting wind energy yield. Application over sites with different terrain features and stability conditions is expected to provide further insight into its application field.