Evaluation of different wind fields for the investigation of the dynamic response of offshore wind turbines

As the size of offshore wind turbines increases, a realistic representation of the spatiotemporal distribution of the incident wind field becomes crucial for modeling the dynamic response of the turbine. The International Electrotechnical Commission (IEC) standard for wind turbine design recommends two turbulence models for simulations of the incident wind field, the Mann spectral tensor model, and the Kaimal spectral and exponential coherence model. In particular, for floating wind turbines, these standard models are challenged by more sophisticated ones. The characteristics of the wind field depend on the stability conditions of the atmosphere, which neither of the standard turbulence models account for. The spatial and temporal distribution of the turbulence, represented by coherence, is not modeled consistently by the two standard models. In this study, the Mann spectral tensor model and the Kaimal spectral and exponential coherence model are compared with wind fields constructed from offshore measurements and obtained from large‐eddy simulations. Cross sections and durations relevant for offshore wind turbine design are considered. Coherent structures from the different simulators are studied across various stability conditions and wind speeds through coherence and proper orthogonal decomposition mode plots. As expected, the standard models represent neutral stratification better than they do stable and unstable. Depending upon the method used for generating the wind field, significant differences in the spatial and temporal distribution of coherence are found. Consequently, the computed structural design loads on a wind turbine are expected to vary significantly depending upon the employed turbulence model. The knowledge gained in this study will be used in future studies to quantify the effect of various turbulence models on the dynamic response of large offshore wind turbines.     

A wind‐tunnel study of the wake development behind wind turbines over sinusoidal hills

In the present work, the wake development behind small‐scale wind turbines is studied when introducing local topography variations consisting of a series of sinusoidal hills. Additionally, wind‐tunnel tests with homogeneous and sheared turbulent inflows were performed to understand how shear and ambient turbulence influence the results. The scale of the wind‐turbine models was about 1000 times smaller than full‐size turbines, suggesting that the present results should only be qualitatively extrapolated to real‐field scenarios. Wind‐tunnel measurements were made by means of stereoscopic particle image velocimetry to characterize the flow velocity in planes perpendicular to the flow direction. Over flat terrain, the wind‐turbine wake was seen to slowly approach the ground while it propagated downstream. When introducing hilly terrain, the downward wake deflection was enhanced in response to flow variations induced by the hills, and the turbulent kinetic energy content in the wake increased because of the speed‐up seen over the hills. The combined wake observed behind 2 streamwise aligned turbines was more diffused and when introducing hills, it was more prone to deflect towards the ground compared to the wake behind an isolated turbine. Since wake interactions are common at sites with multiple turbines, this suggested that it is important to consider the local hill‐induced velocity variations when onshore wind farms are analysed. Differences in the flow fields were seen when introducing either homogeneous or sheared turbulent inflow conditions, emphasizing the importance of accounting for the prevailing turbulence conditions at a given wind‐farm site to accurately capture the downstream wake development.     

Large‐eddy simulations of wind‐farm wake characteristics associated with a low‐level jet

In this study, we performed a suite of flow simulations for a 12‐wind‐turbine array with varying inflow conditions and lateral spacings, and compared the impacts of the flow on velocity deficit and wake recovery. We imposed both laminar inflow and turbulent inflows, which contain turbulence for the Ekman layer and a low‐level jet (LLJ) in the stable boundary layer. To solve the flow through the wind turbines and their wakes, we used a large‐eddy simulation technique with an actuator‐line method. We compared the time series for the velocity deficit at the first and rear columns to observe the temporal change in velocity deficit for the entire wind farm. The velocity deficit at the first column for LLJ inflow was similar to that for laminar inflow. However, the magnitude of velocity deficit at the rear columns for the case with LLJ inflow was 11.9% greater because of strong wake recovery, which was enhanced by the vertical flux of kinetic energy associated with the LLJ. To observe the spatial transition and characteristics of wake recovery, we performed statistical analyses of the velocity at different locations for both the laminar and LLJ inflows. These studies indicated that strong wake recovery was present, and a kurtosis analysis showed that the probability density function for the streamwise velocity followed a Gaussian distribution. In a quadrant analysis of the Reynolds stress, we found that the ejection and sweep motions for the LLJ inflow case were greater than those for the laminar inflow case.     

An experimental investigation on the wake interferences among wind turbines sited in aligned and staggered wind farms

An experimental investigation was conducted for a better understanding of the wake interferences among wind turbines sited in wind farms with different turbine layout designs. Two different types of inflows were generated in an atmospheric boundary layer wind tunnel to simulate the different incoming surface winds over typical onshore and offshore wind farms. In addition to quantifying the power outputs and dynamic wind loads acting on the model turbines, the characteristics of the wake flows inside the wind farms were also examined quantitatively. After adding turbines staggered between the first 2 rows of an aligned wind farm to increase the turbine number density in the wind farm, the added staggered turbines did not show a significant effect on the aeromechanical performance of the downstream turbines for the offshore case. However, for the onshore case, while the upstream staggered turbines have a beneficial effect on the power outputs of the downstream turbines, the fatigue loads acting on the downstream turbines were also found to increase considerably due to the wake effects induced by the upstream turbines. With the same turbine number density and same inflow characteristics, the wind turbines were found to be able to generate much more power when they are arranged in a staggered layout than those in an aligned layout. In addition, the characteristics of the dynamic wind loads acting on the wind turbines sited in the aligned layout, including the fluctuation amplitudes and power spectrum, were found to be significantly different from those with staggered layout.     

Prediction of wind‐turbine fatigue loads in forest regions based on turbulent LES inflow fields

Large‐eddy simulations (LES) were used to predict the neutral atmospheric boundary layer over a sparse and a dense forest, as well as over grass‐covered flat terrain. The forest is explicitly represented in the simulations through momentum sink terms. Turbulence data extracted from the LES served then as inflow turbulence for the simulation of the dynamic structural response of a generic wind turbine. In this way, the impact of forest density, wind speed and wind‐turbine hub height on the wind‐turbine fatigue loads was studied. Results show for example significantly increased equivalent fatigue loads above the two forests. Moreover, a comparison between LES turbulence and synthetically generated turbulence in terms of load predictions was made and revealed that synthetic turbulence was able to excite the same spectral peaks as LES turbulence but lead to consistently lower equivalent fatigue loads. Copyright © 2016 John Wiley & Sons, Ltd.     

风力机三维尾流模型的提出与校核

针对大型风力机叶轮范围内风剪切效应突出以及当前工程尾流模型局限于二维空间而忽略垂直方向风速变化的问题,该文综合考虑风速和湍流强度切变效应对尾流的影响,在前期所发展的二维2D_K Jensen尾流模型的基础上,提出一种新型三维尾流模型。之后,新模型被应用于多种工况条件、多种类型的风力机尾流计算中,较为全面地验证其精度及适用性。通过与外场实测和其他高精度数值模拟的结果进行对比,表明新模型对流向、横风向和垂直向的尾流速度均具有良好的预测精度,将来可应用于大型风电场发电量评估和微观选址工作。     

On wind turbine loads during the evening transition period

The late afternoon hours in the diurnal cycle precede the development of the nocturnal stable boundary layer. This “evening transition” (ET) period is often when energy demand peaks. This period also corresponds to the time of day that is a precursor to late‐afternoon downbursts, a subject of separate interest. To capture physical characteristics of wind fields in the atmospheric boundary layer (ABL) during this ET period, particularly the interplay of shear and turbulence, stochastic simulation approaches, although more tractable, are not suitable. Large‐eddy simulation (LES), on the other hand, may be used to generate high‐resolution ABL turbulent flow fields. We present a suite of idealized LES four‐dimensional flow fields that define a database representing different combinations of large‐scale atmospheric conditions (characterized by associated geostrophic winds) and surface boundary conditions (characterized by surface heat fluxes). Our objective is to evaluate the performance of wind turbines during the ET period. Accordingly, we conduct a statistical analysis of turbine‐scale wind field variables. We then employ the database of these LES‐based inflow wind fields in aeroelastic simulations of a 5‐MW wind turbine. We discuss how turbine loads change as the ET period evolves. We also discuss maximum and fatigue loads on the rotor and tower resulting from different ABL conditions. Results of this study suggest that, during the ET period, the prevailing geostrophic wind speed affects the mean and variance of longitudinal winds greatly and thus has significant influence on all loads except the yaw moment which is less sensitive to uniform and symmetric incoming flow. On the other hand, surface heat flux levels affect vertical turbulence and wind shear more and, as a result, only affect maximum blade flapwise bending and tower fore‐aft bending loads.     

Large‐eddy simulation of stable boundary layer turbulence and estimation of associated wind turbine loads

Stochastic simulation of turbulent inflow fields commonly used in wind turbine load computations is unable to account for contrasting states of atmospheric stability. Flow fields in the stable boundary layer, for instance, have characteristics such as enhanced wind speed and directional shear; these effects can influence loads on utility‐scale wind turbines. To investigate these influences, we use large‐eddy simulation (LES) to generate an extensive database of high‐resolution ( ～ 10 m), four‐dimensional turbulent flow fields. Key atmospheric conditions (e.g., geostrophic wind) and surface conditions (e.g., aerodynamic roughness length) are systematically varied to generate a diverse range of physically realizable atmospheric stabilities. We show that turbine‐scale variables (e.g., hub height wind speed, standard deviation of the longitudinal wind speed, wind speed shear, wind directional shear and Richardson number) are strongly interrelated. Thus, we strongly advocate that these variables should not be prescribed as independent degrees of freedom in any synthetic turbulent inflow generator but rather that any turbulence generation procedure should be able to bring about realistic sets of such physically realizable sets of turbine‐scale flow variables. We demonstrate the utility of our LES‐generated database in estimation of loads on a 5‐MW wind turbine model. More importantly, we identify specific turbine‐scale flow variables that are responsible for large turbine loads—e.g., wind speed shear is found to have a greater influence on out‐of‐plane blade bending moments for the turbine studied compared with its influence on other loads such as the tower‐top yaw moment and the fore‐aft tower base moment. Overall, our study suggests that LES may be effectively used to model inflow fields, to study characteristics of flow fields under various atmospheric stability conditions and to assess turbine loads for conditions that are not typically examined in design standards. Copyright © 2013 John Wiley & Sons, Ltd.     

Large‐eddy simulation of turbulent flow past wind turbines/farms: the Virtual Wind Simulator (VWiS)

A large‐eddy simulation framework, dubbed as the Virtual Wind Simulator (VWiS), for simulating turbulent flow over wind turbines and wind farms in complex terrain is developed and validated. The wind turbines are parameterized using the actuator line model. The complex terrain is represented by the curvilinear immersed boundary method. The predictive capability of the present method is evaluated by simulating two available wind tunnel experimental cases: the flow over a stand‐alone turbine and an aligned wind turbine array. Systematic grid refinement studies are carried out, for both single turbine and multi‐turbine array cases, and the accuracy of the computed results is assessed through detailed comparisons with wind tunnel experiments. The model is further applied to simulate the flow over an operational utility‐scale wind farm. The inflow velocities for this case are interpolated from a mesoscale simulation using a Weather Research and Forecasting (WRF) model with and without adding synthetic turbulence to the WRF‐computed velocity fields. Improvements on power predictions are obtained when synthetic turbulence is added at the inlet. Finally the VWiS is applied to simulate a yet undeveloped wind farm at a complex terrain site where wind resource measurements have already been obtained. Good agreement with field measurements is obtained in terms of the time‐averaged streamwise velocity profiles. To demonstrate the ability of the model to simulate the interactions of terrain‐induced turbulence with wind turbines, eight hypothetical turbines are placed in this area. The computed extracted power underscores the significant effect of site‐specific topography on turbine performance. Copyright © 2014 John Wiley & Sons, Ltd.     

Estimation of turbulence intensity using rotor effective wind speed in Lillgrund and Horns Rev-I offshore wind farms

Turbulence characteristics of the wind farm inflow have a significant impact on the energy production and the lifetime of a wind farm. The common approach is to use the meteorological mast measurements to estimate the turbulence intensity (TI) but they are not always available and the turbulence varies over the extent of the wind farm. This paper describes a method to estimate the TI at individual turbine locations by using the rotor effective wind speed calculated via high frequency turbine data.The method is applied to Lillgrund and Horns Rev-I offshore wind farms and the results are compared with TI derived from the meteorological mast, nacelle mounted anemometer on the turbines and estimation based on the standard deviation of power. The results show that the proposed TI estimation method is in the best agreement with the meteorological mast. Therefore, the rotor effective wind speed is shown to be applicable for the TI assessment in real-time wind farm calculations under different operational conditions. Furthermore, the TI in the wake is seen to follow the same trend with the estimated wake deficit which enables to quantify the turbulence in terms of the wake loss locally inside the wind farm.     

A method for mapping the turbulence intensity and excess energy available to building mounted wind turbines over a UK City

Assessing the potential of proposed urban wind installations is hindered by insufficient assessments of both urban wind resource, and the effectiveness of commercial gust control solutions within built up areas. Evaluating the potential performance of wind turbines within the urban environment requires an estimation of the total energy that would be available to them were effective control systems to be used. This paper presents a methodology for estimating the excess energy content (EEC) present in the gusty urban wind, which is usually under represented when using assessments based only on mean wind speeds. The method is developed using high temporal resolution wind measurements from eight potential turbine sites within the urban and suburban environment. By assessing the relationship between turbulence intensities and the EEC, an analytical methodology for predicting the total wind energy available at a potential turbine site is proposed. Sensitivity analysis with respect to temporal data resolution on the predicted EEC is also demonstrated. The methodology is then integrated with an analytical methodology that was initially developed to predict mean wind speeds at different heights within a UK city based on detailed mapping of its aerodynamic characteristics. Additional estimates of turbulence intensities and EEC based on the current methodology allow a more complete assessment of the wind resource available. The methodology is applied to the UK city of Leeds as a case study and the potential to map turbulence intensities and the total kinetic energy available at different heights within a typical urban city is demonstrated. Copyright © 2015 John Wiley & Sons, Ltd.     

Validation of four LES and a vortex model against stereo-PIV measurements in the near wake of an actuator disc and a wind turbine

In this paper we report the results of a workshop organised by the Delft University of Technology in 2014, aiming at the comparison between different state-of-the-art numerical models for the simulation of wind turbine wakes. The chosen benchmark case is a wind tunnel measurement, where stereoscopic Particle Image Velocimetry was employed to obtain the velocity field and turbulence statistics in the near wake of a two-bladed wind turbine model and of a porous disc, which mimics the numerical actuator used in the simulations. Researchers have been invited to simulate the experimental case based on the disc drag coefficient and the inflow characteristics. Four large eddy simulation (LES) codes from different institutions and a vortex model are part of the comparison. The purpose of this benchmark is to validate the numerical predictions of the flow field statistics in the near wake of an actuator disc, a case that is highly relevant for full wind farm applications. The comparison has shown that, despite its extreme simplicity, the vortex model is capable of reproducing the wake expansion and the centreline velocity with very high accuracy. Also all tested LES models are able to predict the velocity deficit in the very near wake well, contrary to what was expected from previous literature. However, the resolved velocity fluctuations in the LES are below the experimentally measured values.     

Performance and wake comparison of horizontal and vertical axis wind turbines under varying surface roughness conditions

A numerical study of both a horizontal axis wind turbine (HAWT) and a vertical axis wind turbine (VAWT) with similar size and power rating is presented. These large scale turbines have been tested when operating stand‐alone at their optimal tip speed ratio (TSR) within a neutrally stratified atmospheric boundary layer (ABL). The impact of three different surface roughness lengths on the turbine performance is studied for the both turbines. The turbines performance, the response to the variation in the surface roughness of terrain, and the most relevant phenomena involved on the resulting wake were investigated. The main goal was to evaluate the differences and similarities of these two different types of turbine when they operate under the same atmospheric flow conditions. An actuator line model (ALM) was used together with the large eddy simulation (LES) approach for predicting wake effects, and it was implemented using the open‐source computational fluid dynamics (CFD) library OpenFOAM to solve the governing equations and to compute the resulting flow fields. This model was first validated using wind tunnel measurements of power coefficients and wake of interacting HAWTs, and then employed to study the wake structure of both full scale turbines. A preliminary study test comparing the forces on a VAWT blades against measurements was also investigated. These obtained results showed a better performance and shorter wake (faster recovery) for an HAWT compared with a VAWT for the same atmospheric conditions.     

Load alleviation of wind turbines by yaw misalignment

Vertical wind shear is one of the dominating causes of load variations on the blades of a horizontal axis wind turbine. To alleviate the varying loads, wind turbine control systems have been augmented with sensors and actuators for individual pitch control. However, the loads caused by a vertical wind shear can also be affected through yaw misalignment. Recent studies of yaw control have been focused on improving the yaw alignment to increase the power capture at below rated wind speeds. In this study, the potential of alleviating blade load variations induced by the wind shear through yaw misalignment is assessed. The study is performed through simulations of a reference turbine. The study shows that optimal yaw misalignment angles for minimizing the blade load variations can be identified for both deterministic and turbulent inflows. It is shown that the optimal yaw misalignment angles can be applied without power loss for wind speeds above rated wind speed. In deterministic inflow, it is shown that the range of the steady‐state blade load variations can be reduced by up to 70%. For turbulent inflows, it is shown that the potential blade fatigue load reductions depend on the turbulence level. In inflows with high levels of turbulence, the observed blade fatigue load reductions are small, whereas the blade fatigue loads are reduced by 20% at low turbulence levels. For both deterministic and turbulent inflows, it is seen that the blade load reductions are penalized by increased load variations on the non‐rotating turbine parts. Copyright © 2013 John Wiley & Sons, Ltd.     

Incorporating a stochastic data‐driven inflow model for uncertainty quantification of wind turbine performance

Incorporating uncertainty in wind turbine analysis and design is very necessary based on the fact that inherent variability exists in wind turbine systems. Examples of these uncertainties include fluctuations in material properties across turbine blades, variable structure parameters and stochasticity in the inflow—which is considered to be a critical factor affecting the reliability of wind turbines. However, it has been difficult to construct a low‐dimensional yet accurate representation of the stochastic inflow, which precludes rigorous uncertainty propagation and quantification. Recently, we have developed a comprehensive data‐driven approach [called temporal–spatial decomposition (TSD)] for constructing a stochastic, low‐dimensional model that accurately represents stochastic inflow data. We leverage this approach to construct distributional forecasts of key wind turbine performance indicators. To this end, we integrated the stochastic wind model created by the TSD framework with the wind turbine solver FAST. Uncertainty propagation is performed using an adaptive sparse grid collocation approach. We investigate how the order of approximation of the stochastic model affects the quality of the predicted distribution. We observe that the probability distributions of key indicators are not necessarily Gaussian, which has implications for reliability analysis and for failure prediction. Furthermore, the distributions are sensitive to only the first few eigenmodes of the inflow wind model, which indicates that comprehensive uncertainty quantification can potentially be accomplished with moderate computational effort. The approach suggested in this paper enables seamless integration of uncertainty quantification into current deterministic codes for wind turbine simulation and has implications for the design of the next generation of wind turbines including offshore turbines. Copyright © 2017 John Wiley & Sons, Ltd.     

风力机翼型大攻角分离流的数值模拟

为了准确预测风力机翼型在大攻角下分离流动的气动性能,并且为风力机的设计与安全运行提供一种可靠的数值模拟手段,针对某风力机专用翼型,分别采用基于非定常不可压缩Navier-Stokes方程的大涡模拟(LES)模型、RNG k-ε模型和Standard k-ε模型对其气动性能进行数值模拟,并计算出翼型攻角为35～90°,雷诺数为2×106时的气动力参数。将不同湍流模型的计算结果与风洞试验数据进行比较,并分析流场结构。分析结果表明,LES模型能够准确地模拟出翼型表面的分离流动,计算结果与试验数据取得了很好的一致性,并且优于RNG k-ε模型和Standard k-ε模型的模拟结果。     

Effect of winds in a mountain pass on turbine performance

Mountain passes are potentially advantageous sites for the deployment of wind turbines because of road links and electrical transmission infrastructure. However, relatively little is known about wind characteristics and turbine response in these environments. Using hub height wind data from a mountain pass in Switzerland, this paper discusses the causes of the observed pass winds and how a generic wind turbine might perform in those conditions. During 3 months of winter measurements, the winds in the pass showed signatures of forcing by regional pressure gradients rather than local cooling or heating. Turbulence intensity was often less than 10%, and the magnitude of the wind shear power law exponent was less than 0.1. To understand the impact of pass winds on a wind turbine, we simulated a Wind Partnership for Advanced Component Technologies 1.5 MW wind turbine using the Fatigue, Aerodynamics, Structures, and Turbulence (FAST) aeroelastic simulator , forced by artificial wind fields of varying turbulence intensity and shear generated by the turbulence simulator TurbSim. We used the turbine simulation data to train a regression model that is used to predict the turbine response to the pass wind time series. Results showed that depending on long‐term wind characteristics, wind turbines in the pass may perform differently than predicted using a power curve derived from test measurements at another location. This method of generating site‐specific energy capture predictions could be combined with long‐term wind resource data and specific turbine models to better predict the energy production and turbine loads at this, or any other site. Copyright © 2013 John Wiley & Sons, Ltd.     

A stochastic wind turbine wake model based on new metrics for wake characterization

Understanding the detailed dynamics of wind turbine wakes is critical to predicting the performance and maximizing the efficiency of wind farms. This knowledge requires atmospheric data at a high spatial and temporal resolution, which are not easily obtained from direct measurements. Therefore, research is often based on numerical models, which vary in fidelity and computational cost. The simplest models produce axisymmetric wakes and are only valid beyond the near wake. Higher‐fidelity results can be obtained by solving the filtered Navier–Stokes equations at a resolution that is sufficient to resolve the relevant turbulence scales. This work addresses the gap between these two extremes by proposing a stochastic model that produces an unsteady asymmetric wake. The model is developed based on a large‐eddy simulation (LES) of an offshore wind farm. Because there are several ways of characterizing wakes, the first part of this work explores different approaches to defining global wake characteristics. From these, a model is developed that captures essential features of a LES‐generated wake at a small fraction of the cost. The synthetic wake successfully reproduces the mean characteristics of the original LES wake, including its area and stretching patterns, and statistics of the mean azimuthal radius. The mean and standard deviation of the wake width and height are also reproduced. This preliminary study focuses on reproducing the wake shape, while future work will incorporate velocity deficit and meandering, as well as different stability scenarios. Copyright © 2016 John Wiley & Sons, Ltd.     

Investigation of wind turbine wake superposition models using Reynolds‐averaged Navier‐Stokes simulations

It is well accepted that the wakes created by upstream turbines significantly impact on the power production and fatigue loading of downstream turbines and that this phenomenon affects wind farm performance. Improving the understanding of wake effects and overall efficiency is critical for the optimisation of layout and operation of increasingly large wind farms. In the present work, the NREL 5‐MW reference turbine was simulated using blade element embedded Reynolds‐averaged Navier‐Stokes computations in sheared onset flow at three spatial configurations of two turbines at and above rated flow speed to evaluate the effects of wakes on turbine performance and subsequent wake development. Wake recovery downstream of the rearward turbine was enhanced due to the increased turbulence intensity in the wake, although in cases where the downstream turbine was laterally offset from the upstream turbine this resulted in relatively slower recovery. Three widely used wake superposition models were evaluated and compared with the simulated flow‐field data. It was found that when the freestream hub‐height flow speed was at the rated flow speed, the best performing wake superposition model varied depending according to the turbine array layout. However, above rated flow speed where the wake recovery distance is reduced, it was found that linear superposition of single turbine velocity deficits was the best performing model for all three spatial layouts studied.