两种数值模式用于风场模拟的对比研究

以珠海横琴风电场为实例,分别使用线性模型WAsP及基于Fluent的计算流体力学(CFD)模型进行风场模拟及发电量计算,得出两种模型下的计算结果;分别对两者的模拟风速、计算发电量与实际发电量进行比较,并分析误差原因.试验结果表明:对于地形复杂的横琴风电场,WAsP模拟的风速值普遍高于Fluent模拟的风速值;WAsP计算年发电量的误差为21.6％,Fluent的误差为10.4％;基于Fluent的CFD模型在风场模拟中比线性模型WAsP具有更高的准确性.     

地形复杂的风电场资源评估误差分析方法

由于计算模型本身的限制,风能资源地图分析与应用程序(WAsP)不能准确模拟复杂地形中风的流动变化情况,用其评价复杂地形风电场的风能资源时存在一定误差.目前,主要采用RIX方法来评估WAsP在复杂地形中的风速预测误差,长期以来一直缺少根据风速预测误差来评估风电场发电量预测误差的有效方法.文章在RIX方法的基础上,对WAsP应用于复杂地形风电场发电量预测的误差进行了研究,结合工程实践提出了一种发电量误差评估方法;根据某风电场实际发电量数据对提出的评估方法进行了验证,证明其有效性和实用性.     

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。     

基于优化CFD模型的风电场风速数值模拟

为了提高风资源普查的精度,更好地针对我国地形及风况,文章优化了现有风资源计算流体力学模型,并编写了相应计算模块。优化模型包括:(1)贴合复杂山地地形的网格化分器,可以对任意地形进行网格划分;(2)通过分析测风数据自动计算湍流模型系数;(3)增加温度运输方程,将大气边界层热稳定度耦合到动量方程和湍流模型中;(4)与实际大气边界层热稳定度分层效应一致的入口条件及壁面函数。为了验证优化后的风资源计算方法的精度,文章对一待开发风电场进行了风资源计算。计算结果显示,使用优化的模块可以更精确地计算风速,与优化前相比,可以将误差至少降低10%。     

利用WAsP软件对风力机发电量的预测

提出了一种利用WAsP软件准确求解风力机发电量的方法。首先,利用WAsP OWC Wizard工具对欲安装风力机地区一年的风速资料进行分析,得到风谱图;其次,在给定地区的数字地图上建立模拟风力机站;然后,根据风力发电机的功率特性,采用WAsP Turbine Editor工具拟合输出功率特性曲线;最后,通过实例详细阐述风力机发电量的计算方法。由于这种计算方法完全建立在风场风谱图和风力机自身输出特性的基础上,因此得到的风力机发电量的计算结果更加准确。在WAsP软件的协助下,计算过程可以得到很大简化,能够满足工程的应用。     

风电场局部地形改造空气动力场数值模拟

针对风场局部地区机组发电能力不足、技术改造效果不确定的问题,应用Global mapper、WAsP、Matlab等软件拟合出局部地形物理模型。以风场实测数据作为物理模型的入流条件,利用UDF自定义速度入口和湍流入口,通过CFD计算出12种来流下各改造地形的机组轮毂高度处的风速、湍流度。与实际风数据比较后发现,该物理模型具有较高的精度,同时改造地形可有效增加下风向的风速、减小湍流度,从而取得较好的经济效益。     

基于大涡模拟与中尺度数值天气模式的精细化风场模拟

[目的]结合中尺度模型与大涡模拟模型,考虑大气边界层变化,开展了亚公里级的项目机组排布的数值模拟,给海上风机项目在选址排布阶段提供发电效能高的排布方案。[方法]将中尺度数值天气模拟结果转换为大涡模拟模型输入的边界条件,并在大涡模拟模型中引入实际风电场运行的模型参数,进行考虑实际大气边界层变化下的风电场空间的环境风场数值模拟试验,基于风电场收集的观测数据,对本风场精细化模拟方案的结果进行评估。[结果]模拟结果表明：将中尺度天气模型的模拟结果转换为大涡模拟模型能读取的动态驱动并基于该模型对风电场所处的风场进行模拟,其模拟结果能再现在实际风电场中,外部风场流经风电场后,外部风场的变化和在风电机群内所产生的尾流及其对于风电场内部风场的影响,且在风机轮毂处的风速模拟值的均方根误差为1.54 m/s。[结论]该考虑中尺度气象要素变化和风电场对环境风场影响的风场精细化模拟方案可为实际项目设计阶段提供相应的指导。     

中国风能开发利用的风环境区划

基于大气边界层气象和气候学理论分析以及中尺度数值模拟,采用秒级探空气象资料和典型地形激光雷达观测资料,依据风能利用高度内总体风能资源开发潜力,划分出9个风环境区。年平均风能环境指数最高的风环境区是北方通风廊道,其次是东北平原,最低的是青藏高原下游地区。发现在稳定大气条件下,风能利用高度内的平均风速垂直变化呈两层分布形态,下层平均风速随高度的增速比上层大2～5倍。下层风速的垂直变化取决于地表特征,上层则受上游大地形造成的局地环流影响,由此形成不同风环境区风能资源特性的差异。最后给出构建不同地形条件下平均风廓线计算方法的建议。结论可为中国风能资源评估理论拓展与数值模拟、风电场选址和适用复杂地形条件的风电机组设计提供科学支撑。     

南澳岛风电场流场的数值计算

采用RANS模型和标准k-ε湍流模型研究南澳岛风场的风速分布。基于中性大气稳定性条件,对来流风廓线、湍流模型参数和壁面函数进行分析,将风向划分为16个扇区,进行多风向流场数值计算。基于实际测风数据的时间序列和流场数值计算结果,建立全风向月(年)平均风速的计算方法。利用风场内6个测风塔上12个测点的实测数据,对数值计算结果进行对比分析,所有测点的平均计算偏差为4.41%,其中与基准点相同高度的测点计算偏差均在3.7%以内。     

低风速风电场风电机组的粗糙度取值研究

基于风资源分析软件WAsP中的粗糙度理论,推导风电机组粗糙度的计算公式,通过与建立风电机组模型的结果比较验证公式有效性,据此分析低风速风电场中风电机组粗糙度的影响因素,结果表明,设置风电机组粗糙度和建立风电机组模型对发电量的影响机理不同,风电机组的粗糙度与风电场平均风速、叶轮直径和轮毂高度呈正相关,与风电机组额定风速和风电机组间距呈负相关。     

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.     

Influence of atmospheric stability on wind turbine loads

Simulations of wind turbine loads for the NREL 5 MW reference wind turbine under diabatic conditions are performed. The diabatic conditions are incorporated in the input wind field in the form of wind profile and turbulence. The simulations are carried out for mean wind speeds between 3 and 16 m s ^{? 1} at the turbine hub height. The loads are quantified as the cumulative sum of the damage equivalent load for different wind speeds that are weighted according to the wind speed and stability distribution. Four sites with a different wind speed and stability distribution are used for comparison. The turbulence and wind profile from only one site is used in the load calculations, which are then weighted according to wind speed and stability distributions at different sites. It is observed that atmospheric stability influences the tower and rotor loads. The difference in the calculated tower loads using diabatic wind conditions and those obtained assuming neutral conditions only is up to 17%, whereas the difference for the rotor loads is up to 13%. The blade loads are hardly influenced by atmospheric stability, where the difference between the calculated loads using diabatic and neutral input wind conditions is up to 3% only. The wind profiles and turbulence under diabatic conditions have contrasting influences on the loads; for example, under stable conditions, loads induced by the wind profile are larger because of increased wind shear, whereas those induced by turbulence are lower because of less turbulent energy. The tower base loads are mainly influenced by diabatic turbulence, whereas the rotor loads are influenced by diabatic wind profiles. The blade loads are influenced by both, diabatic wind profile and turbulence, that leads to nullifying the contrasting influences on the loads. The importance of using a detailed boundary‐layer wind profile model is also demonstrated. The difference in the calculated blade and rotor loads is up to 6% and 8%, respectively, when only the surface‐layer wind profile model is used in comparison with those obtained using a boundary‐layer wind profile model. Finally, a comparison of the calculated loads obtained using site‐specific and International Electrotechnical Commission (IEC) wind conditions is carried out. It is observed that the IEC loads are up to 96% larger than those obtained using site‐specific wind conditions.Copyright © 2012 John Wiley & Sons, Ltd.     

内蒙古地区风资源评估与风场特征风速的推导

对内蒙古二十四个地区的风能资源进行评估,得到风谱图.首先提出了利用WAsP软件对1998年至2008年期间内蒙古二十四地区的风资源资料中的基础进行分析;然后利用风速威布尔分布函数和风力发电机组输出功率的威布尔的概率密度函数,求两个函数的极值,推导出切入风速和额定风速的公式.最后以内蒙古六个地区为例,计算不同风资源条件下的切入风速和额定风速.     

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%。     

低空急流条件下水平轴风力机风轮气动特性的研究

为阐明低空急流条件下风力机风轮的气动特性,基于工程化的边界层风速模型和Von Karman谱模型建立不同来流的脉动风场,对比研究低空急流条件下NREL 5 MW风力机风轮的输出功率和气动载荷的变化规律。结果表明:如果仅以轮毂高度处的风速作为风力机变桨控制的依据,与均匀来流和剪切来流相比较,低空急流条件下,虽然来流风功率明显增大,但风轮的输出功率在较高风速时反而减小;风轮所受的不平衡气动载荷,包括横向力、纵向力、偏航力矩和倾覆力矩在较高风速时小于剪切来流的结果;且仅以轮毂高度处的风速预测得到的风轮输出功率高于实际结果,其最大相对误差为89.4%。因此,低空急流条件下,为提高风能利用率和风轮输出功率的预测精度,应考虑不同高度位置处的风速大小对风力机进行变桨控制和功率预测。     

Evaluation of wind farm efficiency and wind turbine wakes at the Nysted offshore wind farm

Here, we quantify relationships between wind farm efficiency and wind speed, direction, turbulence and atmospheric stability using power output from the large offshore wind farm at Nysted in Denmark. Wake losses are, as expected, most strongly related to wind speed variations through the turbine thrust coefficient; with direction, atmospheric stability and turbulence as important second order effects. While the wind farm efficiency is highly dependent on the distribution of wind speeds and wind direction, it is shown that the impact of turbine spacing on wake losses and turbine efficiency can be quantified, albeit with relatively large uncertainty due to stochastic effects in the data. There is evidence of the ‘deep array effect’ in that wake losses in the centre of the wind farm are under‐estimated by the wind farm model WAsP, although overall efficiency of the wind farm is well predicted due to compensating edge effects. Copyright © 2010 John Wiley & Sons, Ltd.     

Production of the Finnish Wind Atlas

The Finnish Wind Atlas was prepared applying the mesoscale model AROME with 2.5 km horizontal resolution and the diagnostic downscaling method Wind Atlas Analysis and Application Programme (WAsP) with 250 m resolution. The latter was applied for areas most favourable for wind power production: a 30 km wide coastal/offshore zone, highlands, large lakes and large fields. The methodology included several novel aspects: (i) a climatologically representative period of real 48 months during 1989–2007 was simulated with the mesoscale model; (ii) in addition, the windiest and calmest months were simulated; (iii) the results were calculated separately for each month and for sectors 30° wide; (iv) the WAsP calculations were based on the mesoscale model outputs; (v) in addition to point measurements, also radar wind data were applied for the validation of the mesoscale model results; (vi) the parameterization method for gust factor was extended to be applicable at higher altitudes; and (vii) the dissemination of the Wind Atlas was based on new technical solutions. The AROME results were calculated for the heights of 50, 75, 100, 125, 150, 200, 300 and 400 m, and the WAsP results for the heights of 50, 75, 100, 125 and 150 m. In addition to the wind speed, the results included the values of the Weibull distribution parameters, the gust factor, wind power content and the potential power production, which was calculated for three turbine sizes. The Wind Atlas data are available for each grid point and can be downloaded free of charge from dynamic maps at www.windatlas.fi . Copyright © 2011 John Wiley & Sons, Ltd.     

Non‐uniform velocity distribution effect on the Betz–Joukowsky limit

A simple analytic correction is derived for the maximum efficiency of an ideal wind turbine rotor, the Betz–Joukowsky limit. The analytic correction accounts for the effect that the non‐uniform atmospheric boundary layer velocity distribution has on the Betz–Joukowsky derivation. The maximum power coefficient predicted by using the atmospheric boundary layer velocity profile is slightly higher than that predicted by using a uniform velocity distribution. The application of the correction to a 100 m rotor diameter at 80 m hub height in a neutrally stratified boundary layer flow predicts a maximum power increase of 1–2%, depending on the approach terrain. Copyright © 2012 John Wiley & Sons, Ltd.     

风电场50年一遇安全风速计算方法的对比分析

利用耿贝尔Ⅰ型极值概率法和MeteodynWT软件(CFD模型),结合气象站与风电场的风速关系,推算了不同复杂程度的风电场轮毂高度处50年一遇的安全风速,并将计算结果进行对比分析。分析结果显示:2种方法计算得到的风电场极大风速存在一定的差别;对于平坦地区,耿贝尔Ⅰ型极值概率法计算得到的极大风速与MeteodynWT推算结果相差较小,但对于一些复杂地区,2种方法计算得到的极大风速结果相差很大。