边界层气象对风电机组功率特性的影响研究

边界层气象因素对运行过程中的风电机组性能和表现具有重要影响。文章利用激光雷达设备对某大型风电场开展了气象观测,针对不同湍流、风切变、日变化和降雨情况下风电机组的功率特性进行了研究。研究结果表明:高湍流在切入风速和额定风速左右会提升或降低机组的功率曲线,并增大输出功率的离散性;高切变在切入风速和额定风速左右均会提升机组的功率曲线,并减小输出功率的离散性;边界层的湍流和风切变等气象要素存在显著的日变化规律,并影响风电机组的功率曲线和输出功率的离散性,表现出日夜不同;降雨天气与非降雨天气相比,总体上提升了机组的功率曲线,并增大了输出功率的离散性。文章的研究结果可为风电项目评估发电量、风电场功率预报等工作提供参考。     

基于致动线和大涡模拟的不同入流风况下的风力机尾流特性研究

风力机的尾流效应是风电场规划与设计中需要考虑的重要因素之一,准确评估风电场的尾流效应对于风电场微观选址、保障机组运行安全、提高风电场经济效益有着重要的意义。文章以NREL 5 MW风力机为对象,基于致动线和大涡模拟方法,研究其在均匀入流和切变入流等不同风况下风力机的尾流特性,入流风况分别为在不同风速下的均匀大气入流和在不同地表粗糙度情况下的切变大气入流。研究结果表明:入流风速增大,尾流区螺旋状叶尖涡的涡间距增大,尾流速度恢复的距离越长;地表粗糙度长度增加,在塔筒竖直方向内相同高度对应的风速减小,导致塔筒产生的阻力减小,风力机塔筒形成的涡更容易发生脱落和破裂,进而导致脱落涡的涡量值增加。研究结果有助于准确地理解风力机尾流发展变化规律,为风电场微观选址、风力机功率预测等工作提供理论支持。     

空冷岛对底层大气边界层特性的影响

利用Gambit软件建立空冷岛模型,通过UDF加载大气边界层函数,并针对不同温度层结设置不同的温度分布函数,利用Fluent软件模拟并分析空冷岛对局地环境的影响,得到空冷岛周围区域不同高度处的风速和温度值,结合风切变理论和Monin-Obukhov相似理论计算空冷岛周围动量通量和热量通量的变化.结果表明:随着风速的增大,风切变指数会略微增大,且沿流向逐渐减小;空冷岛对动量通量和热量通量的影响沿流向逐渐减小,且稳定层结下的影响小于不稳定层结下的影响,即使在稳定层结下,空冷岛周围动量通量和热量通量的变化程度亦均大于其他区域动量通量和热量通量的变化程度.     

高原山地风电场脉动风特性实测研究

文章基于四川省高原山地风电场实测风速数据,对平均风速与风向、湍流强度、阵风因子、湍流积分尺度等脉动风特性进行了研究分析。研究结果表明:当平均风速较低时,随着风速的增大,湍流强度和阵风因子均呈现出明显的减小趋势,而当风速超过临界值时,减小趋势逐渐平缓,两者的临界风速分别为8 m/s和10m/s;随着湍流强度的增大,阵风因子呈现增大的趋势,且随实测高度增加两者相关性更好;随着平均风速的增大,湍流积分尺度呈现增大的趋势,实测数据计算得到的30 m和80 m高度层顺风方向湍流积分尺度分别为114.8 m和168.7 m,更为接近日本风载荷规范计算结果。     

基于分形学的湍流风谱特性对比研究

为定量描述湍流风场和解决湍流风谱模型选择时存在盲目性和不准确性等问题,基于多种湍流风谱模型并考虑湍流强度、地表粗糙度及空间高度的影响,生成不同程度的风速时间序列曲线。基于图像识别技术和计盒维数法对各条件下风速曲线分形维数进行计算。结果表明:不同湍流风谱模型具有不同的分形特性;湍流风分形特性受湍流强度和地表粗糙度影响;相同地貌特征和气候条件下,不同高度处风速时间序列数据具有不同的分形维数。     

基于物理原理的风电场短期风速预测研究

对符合功率预测要求的短期风速预测进行研究,提出了基于物理原理的预测方法,该方法以数值天气预报(Numerical-Weather-Prediction,NWP)风速为输入数据,采用粗糙度变化模型与地形变化模型反映风电场局地效应对大气边界层风的影响;通过与不同风况下的实测风速进行比较,表明预测结果基本能满足预测精度的要求,但预测准确性会随风速变化剧烈程度的增强而有所降低;根据误差分析,NWP风速的准确性是影响预测结果的最主要因素。     

湍流风分形特性对比研究

基于多种湍流风谱模型并考虑湍流强度、地表粗糙度及空间高度对湍流风的影响,获取各种环境条件下的湍流风风速时间序列数据并生成相应的时域风速曲线。应用图像识别技术和盒子计数法,对各风速曲线分形维数进行计算与分析。结果可揭示湍流风风速时间序列的分形特性,且研究了湍流强度、地表粗糙度及空间高度对湍流风分形维数产生的影响,可为湍流风的分析及湍流风谱的准确选择提供一种新思路。     

3.5 m×2.5 m风洞大气边界层被动模拟实验研究

为实现风洞大气边界层的准确模拟,提出一种综合多个参数几何比例一致性分析的风洞大气边界层被动模拟实验。实验布置8种不同湍流发生装置布局的工况以模拟不同地形,通过对平均风速、湍流强度、湍流积分尺度以及功率谱随高度变化的测量,开展考虑模拟地面粗糙度与大气边界层高度比例因子一致性的大气边界层模拟研究,并对比分析尖劈和粗糙元对模拟流场的影响。实验研究表明:在3.5 m×2.5 m风洞中建立的模拟大气边界层流场,可实现符合ASCE 7标准比例因子为1∶200的A地形及符合RLB-AJJ标准比例因子为1∶300的B地形模拟。     

大气湍流稳定度对风力机尾流影响的模拟研究

利用Agel-TDM风力机动力模型,模拟分析不同湍流状态下单台风力机尾流的空间特征。研究结果表明:随着大气湍流稳定性增强,在水平方向上风力机尾流效应减弱,即风力机尾流距离减小;在铅垂方向,动量垂直输送加快,增强了风力机下游风速的恢复速率,风电场的实际出力能力提高。     

台风过境风电场的CFD数值模拟方法研究

以台风烟花过境沿海陆上风电场期间,风电场内特别是风电机组位置的风速、风向和湍流强度的微尺度分布及其随时间的变化为研究对象,基于中尺度WRF模式的模拟结果建立CFD计算域的边界条件,建立台风大气边界层风速、风向廓线参数化模型,以及考虑中尺度热带气旋和台风边界层内卷效应的风场CFD计算模型。算例结果表明,所建立的台风大气边界层风场CFD计算模型可反映台风大气涡旋在风电场微尺度范围的流动特性,风电机组轮毂点的计算风速和实测的机舱风速符合较好,表明所研究的方法可进一步应用于台风影响地区风电场内风电机组台风风险的精细化评估,开展考虑台风风险的微观选址优化等。     

风电场中多台风力机的数值模拟

将NREL 5 MW风力机作为基本机型,使用致动线模型和大涡模拟相结合的数值方法,在中性大气边界层中模拟含有多台风力机的风电场。为了模拟风电场的复杂入流条件,首先模拟体积为3000 m(长)×3000 m(宽)×1000m(高)的大气边界层,并对模拟结果进行验证,结果表明:在覆盖逆温层以下,不同高度处的位温不变,平均风速满足剪切特性,脉动风速满足湍流谱特性;然后,分析了致动线模型中风轮直径上的网格节点数量(N)和高斯分布因子(ε)的取值规律,发现ε以网格尺度(η)为自变量取值时,N越大,η的系数越大,当N取63时,η的系数可取2或3,但N取25时,η只能取1.2;最后,使用致动线模型在大气边界层中布置8台风力机,模拟风电场,并对风力机间的相互干扰进行分析,发现第一排风力机功率明显大于其他风力机功率输出,占风场总功率输出的40.3%。     

Atmospheric stability‐dependent infinite wind‐farm models and the wake‐decay coefficient

We extend the infinite wind‐farm boundary‐layer (IWFBL) model of Frandsen to take into account atmospheric static stability effects. This extended model is compared with the IWFBL model of Emeis and to the Park wake model used in Wind Atlas Analysis and Application Program (WAsP), which is computed for an infinite wind farm. The models show similar behavior for the wind‐speed reduction when accounting for a number of surface roughness lengths, turbine to turbine separations and wind speeds under neutral conditions. For a wide range of atmospheric stability and surface roughness length values, the extended IWFBL model of Frandsen shows a much higher wind‐speed reduction dependency on atmospheric stability than on roughness length (roughness has been generally thought to have a major effect on the wind‐speed reduction). We further adjust the wake‐decay coefficient of the Park wake model for an infinite wind farm to match the wind‐speed reduction estimated by the extended IWFBL model of Frandsen for different roughness lengths, turbine to turbine separations and atmospheric stability conditions. It is found that the WAsP‐recommended values for the wake‐decay coefficient of the Park wake model are (i) larger than the adjusted values for a wide range of neutral to stable atmospheric stability conditions, a number of roughness lengths and turbine separations lower than ～ 10 rotor diameters and (ii) too large compared with those obtained by a semiempirical formulation (relating the ratio of the friction to the hub‐height free velocity) for all types of roughness and atmospheric stability conditions. © 2013 The Authors. Wind Energy published by John Wiley & Sons, Ltd.     

Impact of wall roughness and turbulence level on the performance of a horizontal axis wind turbine with the U‐RANS solver THETA

The presented work investigates the impact of different sheared velocity profiles in the atmospheric boundary layer on the characteristics of a wind turbine by modifying the wall roughness coefficients in the logarithmic velocity profile. Moreover, the rotor and wake characteristics in dependence of the turbulence boundary conditions are investigated. In variant I, the turbulence boundary conditions are defined in accordance to the logarithmic velocity profile with different wall roughness lengths. In variant II, the turbulent kinetic energy and turbulent viscosity remain independent of the velocity profile and represent the free‐stream turbulence level. With an increase of the shear in the velocity profile, the amplitudes in the 3/rev characteristics of rotor thrust and rotor torque, induction factors, and effective angles of attack are increased. In variant I, the overall levels of thrust coefficient are hardly affected by the velocity profiles resulting from different wall roughness length values. The power coefficient is reduced about 1%. Conversely, compared with variant II, a difference of 2% in the power coefficient has been detected. Moreover, the wake recovery process strongly depends on the turbulence boundary condition. Simulations are carried out on an industrial 900‐kW wind turbine with the incompressible U‐RANS solver THETA.     

Estimation of offshore extreme wind from wind‐wave coupled modeling

A coupledwind‐wave modeling system is used to simulate 23 years of storms and estimate offshore extreme wind statistics. In this system, the atmospheric Weather Research and Forecasting (WRF) model and Spectral Wave model for Near shore (SWAN) are coupled, through a wave boundary layer model (WBLM) that is implemented in SWAN. The WBLM calculates momentum and turbulence kinetic energy budgets, using them to transfer wave‐induced stress to the atmospheric modeling. While such coupling has a trivial impact on the wind modeling for 10‐m wind speeds less than 20 ms^?1, the effect becomes appreciable for stronger winds—both compared with uncoupled WRF modeling and with standard parameterization schemes for roughness length. The coupled modeling output is shown to be satisfactory compared with measurements, in terms of the distribution of surface‐drag coefficient with wind speed. The coupling is also shown to be important for estimation of extreme winds offshore, where the WBLM‐coupled results match observations better than results from noncoupled modeling, as supported by measurements from a number of stations.     

ENDOW (efficient development of offshore wind farms): modelling wake and boundary layer interactions

While experience gained through the offshore wind energy projects currently operating is valuable, a major uncertainty in estimating power production lies in the prediction of the dynamic links between the atmosphere and wind turbines in offshore regimes. The objective of the ENDOW project was to evaluate, enhance and interface wake and boundary layer models for utilization offshore. The project resulted in a significant advance in the state of the art in both wake and marine boundary layer models, leading to improved prediction of wind speed and turbulence profiles within large offshore wind farms. Use of new databases from existing offshore wind farms and detailed wake profiles collected using sodar provided a unique opportunity to undertake the first comprehensive evaluation of wake models in the offshore environment. The results of wake model performance in different wind speed, stability and roughness conditions relative to observations provided criteria for their improvement. Mesoscale model simulations were used to evaluate the impact of thermal flows, roughness and topography on offshore wind speeds. The model hierarchy developed under ENDOW forms the basis of design tools for use by wind energy developers and turbine manufacturers to optimize power output from offshore wind farms through minimized wake effects and optimal grid connections. The design tools are being built onto existing regional‐scale models and wind farm design software which was developed with EU funding and is in use currently by wind energy developers. Copyright © 2004 John Wiley & Sons, Ltd.     

Analytical model for predicting the performance of arbitrary size and layout wind farms

A simple engineering model for predicting wind farm performance is presented, which is applicable to wind farms of arbitrary size and turbine layout. For modeling the interaction of wind farm with the atmospheric boundary layer (ABL), the wind farm is represented as added roughness elements. The wind speed behind each turbine is calculated using a kinematic model, in which the friction velocity and the wind speed outside the turbine wake, constructed based on the wind farm‐ABL interaction model, are employed to estimate the wake expansion rate in the crosswind direction and the maximum wind speed that can be recovered within the turbine wake, respectively. Validation of the model is carried out by comparing the model predictions with the measurements from wind tunnel experiments and the Horns Rev wind farm. For all validation cases, satisfactory agreement is obtained between model predictions and experimental data. Copyright © 2015 John Wiley & Sons, Ltd.     

Assessing atmospheric stability and its impacts on rotor‐disk wind characteristics at an onshore wind farm

As the average hub height and blade diameter of new wind turbine installations continue to increase, turbines typically encounter higher wind speeds, which enable them to extract large amounts of energy, but they also face challenges due to the complex nature of wind flow and turbulence in the planetary boundary layer (PBL). Wind speed and turbulence can vary greatly across a turbine's rotor disk; this variability is partially due to whether the PBL is stable, neutral or convective. To assess the influence of stability on these wind characteristics, we utilize a unique data set including observations from two meteorological towers, a surface flux tower and high‐resolution remote‐sensing sound detection and ranging (SODAR) instrument. We compare several approaches to defining atmospheric stability to the Obukhov length (L). Typical wind farm observations only allow for the calculation of a wind shear exponent (α) or horizontal turbulence intensity (I_U) from cup anemometers, whereas SODAR gives measurements at multiple heights in the rotor disk of turbulence intensity (I) in the latitudinal (I_u), longitudinal (I_v) and vertical (I_w) directions and turbulence kinetic energy (TKE). Two methods for calculating horizontal Ifrom SODAR data are discussed. SODAR stability parameters are in high agreement with the more physically robust L,with TKE exhibiting the best agreement, and show promise for accurate characterizations of stability. Vertical profiles of wind speed and turbulence, which likely affect turbine power performance, are highly correlated with stability regime. At this wind farm, disregarding stability leads to over‐assessments of the wind resource during convective conditions and under‐assessments during stable conditions. Copyright © 2011 John Wiley & Sons, Ltd.     

Turbulent kinetic energy estimates from profiling wind LiDAR measurements and their potential for wind energy applications

This study shows that turbulent kinetic energy (TKE) estimates, derived from static LiDARs in Doppler Beam Swing (DBS) mode, permit a qualitative and quantitative characterization and analysis of turbulent structures as wind turbine wakes, and convective or shear generated eddies in the lower atmospheric boundary layer. The analysed data, collected by a WINDCUBE™ v1 in a wind park in Austria, is compared to WINDCUBE™ v1 and sonic data from the WINd Turbine Wake EXperiment Wieringermeer (WINTWEX-W). Although turbulence measurements with a WINDCUBE™ v1 are limited to a specific length scale, processed measurements above this threshold are in a good agreement with sonic anemometer data. In contrast to the commonly used turbulence intensity, the calculation of TKE not only provides an appropriate measure of turbulence intensities but also gives an insight into its origin. The processed data show typical wake characteristics, as flow decelerations, turbulence enhancement and wake rotation. By comparing these turbulence characteristics to other turbulent structures in the atmospheric boundary layer, we found that convection driven eddies in the surface layer have similar turbulence characteristics as turbine wakes, which makes convective weather situations relevant for wind turbine fatigue considerations.     

Effects of inflow turbulence intensity and turbine arrangements on the power generation efficiency of large wind farms

In this study, we conduct a series of large‐eddy simulations (LESs) to study the impact of different incoming turbulent boundary layer flows over large wind farms, with a particular focus on the overall efficiency of electricity production and the evolution of the turbine wake structure. Five representative turbine placements in the large wind farm are considered, including an aligned layout and four staggered layouts with lateral or vertical offset arrangements. Four incoming flow conditions are used and arranged from the LESs of the ABL flow over homogeneous flat surfaces with four different aerodynamic roughness lengths (i.e., z0 = 0.5, 0.1, 0.01, and 0.0001 m), where the hub‐height turbulence intensity levels are about 11.1%, 8.9%, 6.8%, and 4.9%, respectively. The simulation results indicate that an enhancement in the inflow turbulence level can effectively increase the power generation efficiency in the large wind farms, with about 23.3% increment on the overall farm power production and up to about 32.0% increment on the downstream turbine power production. Under the same inflow condition, the change of the turbine‐array layouts can increase power outputs within the first 10 turbine rows, which has a maximum increment of about 26.5% under the inflow condition with low turbulence. By comparison, the increase of the inflow turbulence intensity facilitates faster wake recovery that raises the power generation efficiency of large wind farms than the adjustment of the turbine placing layouts.