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.     

WEPAS和WAsP在复杂地形条件下的适应性

利用WEPAS和WAsP软件分别计算了南澳风电场的发电量,在充分考虑岛屿型复杂地形地貌条件下对2个软件的计算结果进行对比分析,研究表明,对于地形复杂的南澳风电场,WEPAS和WAsP软件发电量计算结果与实际发电量差值分别为-15.18%和28.02%。其中,WEPAS软件计算的风电场风速和风功率密度上下限偏差较小,结果比较平滑;WAsP软件计算结果比实际值偏高,但是单台风机平均风速和发电量计算结果与实际风况变化趋势比较一致。对上述结论的可能原因进行分析,初步显示2种软件的风场风况计算模式在复杂地形条件下存在较大的不足,风场诊断模式不能较好地模拟复杂地形条件下大气边界层风廓线的实际流动状况。因此,需要改进模式,研发出适用于大气边界层流动计算的风廓线模型、湍流模型和地表函数模型。     

实际地形风场CFD模拟中粗糙度的影响分析

以苏格兰Askervein小山为例,基于Fluent流体计算平台,结合NURBS地形建模方法,模拟了实际地形下的风场分布.分别在粗糙度长度为0～0.05m的情况下进行CFD风场模拟试验.与实测的风速加速比进行比较,发现模拟所得的风速加速比均方根误差在迎风面保持在6%以内,而背风面最大为26.56%,表明粗糙度条件对CFD风场模拟结果有较大影响.实现了用于CFD风场模拟的下垫面粗糙度精细化方法.在迎风坡和背风坡设置不同粗糙度长度的情况下,风速加速比均方根误差减小为7.42%,模拟结果在背风坡区域有明显改善.最后指出,在进行复杂地形风场CFD数值模拟时,有必要进行粗糙度条件的精细化设置.     

威布尔拟合方法对海上风场发电量评估的影响研究

为研究风速威布尔参数不同造成的海上风机发电量差异,分别对比了极大似然法、最小二乘法、WAsP法和实证法四种风速威布尔拟合方法。通过统计不同风向扇区数的评估结果,得出合理的风向扇区数范围为16至36。此外还重点分析了四种拟合方法计算得出的海上风场发电量、尾流损失值,并与时序计算值进行了对比,结果显示威布尔分布可较好地描述海上风资源情况,能精准预测海上风场的发电量。四种方法中,极大似然法和WAsP方法为最优方法,最小二乘法其次,实证法精度最低。     

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。     

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。     

风电机组叶片加装涡流发生器提升发电能力试验研究

涡流发生器是风电机组中控制流动分离的一种有效方式。以目标风场的具体空气密度、风况等作为主要输入,对拟加装的涡流发生器的形状和安装位置进行模拟仿真,获得理论最优解,仿真计算发电量可提升1%。涡流发生器在风电场进行现场安装试验,对运行整1年的运行数据进行分析,分别对加装涡流发生器的机组和相邻对照组从实际发电量、实际功率曲线估算应发电量等方面评估发电量增益情况。结果表明:在年平均风速为5.06 m/s时,通过加装涡流发生器,发电量提升约为1%,与前期仿真计算值相符合;此外,由于湍流、偏航误差等的存在,加装涡流发生器后,功率值提升的风速范围较理论值有所拓宽。     

风资源评估中WindSim水平分辨率的敏感性试验

利用基于计算流体力学(CFD) 的风能资源评估系统软件WindSim,在不同水平网格分辨率条件下对我国黄土高原地区陕西靖边县境内某风电场2010年7月~2011年6月的风资源情况进行了模拟,并将模拟结果与测风塔观测结果进行了对比分析。结果表明,在低水平网格分辨率下,WindSim对风能资源的空间分布模拟主要以海拔高度为基础,对局地地形的影响并不能很好地反映,模拟风速误差较大;提高分辨率后,对风能资源空间分布的模拟能力明显提高,模拟风速的误差也显著减小。但不同分辨率下的风速频率和风向频率分布并无显著差别,不能很好地体现出风能特性。通过估算发电量发现,输入不同测风塔资料得到的发电量差异较大,说明在地形较为复杂的风电场,应多布设测风塔,以期得到较为准确的发电量结果。     

基于风轮面等效风速的风电场发电量评估方法研究

基于风轮面等效风速(REWS),建立考虑风切变影响的发电量评估方法。首先,根据CFD仿真技术建立风电场风切变计算方法,并通过实际风电场案例验证该方法的可靠性;其次,通过风电场案例分析传统发电量评估方法由于未考虑风切变的影响而产生的误差,该误差在湍流强度为0.14时高达4.33%,在湍流强度0.18时高达4.51%,因此在评估过程中不能被忽略。最后,采用REWS定义功率曲线建立新的发电量评估方法。风电场验证结果表明,新方法将风切变引起的发电量误差从4.51%降到了0.62%,具有较高的计算精度。     

A WIND-POWER FORECAST SYSTEM BASED ON THE METEOROLOGICAL NUMERICAL MODEL

基于高分辨率中尺度气象模式,利用卡尔曼滤波订正技术和经验统计规律订正技术,通过动态加入实时观测资料对数值模式预报风速进行滚动订正,建立基于气象数值模式的风电功率预测系统,开展风电场未来72h风速及风电功率预测.利用该系统在上海崇明风电场进行为期两个月的预报试验,结果表明:数值模式预报风速与观测值之间的误差随着预报时效增长逐渐加大,并在不同时段模式的系统误差分布规律也有所差别,模式预报风速与误差之间有一定的统计关系.经过滚动订正预报模型订正后,预报发电量误差比模式本身预报发电量误差明显减小,风速及发电功率预报质量明显提高.     

Wind energy resources on Phuquoc Island,Vietnam

This study analyzes the wind energy resources on Phuquoc Island, Vietnam. Daily wind data are collected from 2005 to 2011 in this study. The annual mean speed and power density are 6 m/s and 355 W/m², respectively. Results show that more than 35% of the wind energy comes from the northwest. In this study, a 75 MW wind farm with 25 wind turbines is simulated by using the WAsP 10 program. The wind farm can produce over 189.636 GWh annually. In addition, the effects of wind disturbance and three-phase short circuit of the grid are analyzed using the ETAP 7.0 program.     

利用STRM和NCEP数据提高风电场宏观选址的准确性

利用STRM数据得出风电场宏观地形,利用NCEP数据提取出风电场的气象数据,在WASP813软件的支持下,计算出描述风资源概况的风速、风功率密度和威布尔分布参数值的分布概况。根据风速、风功率密度分布可以直观地看出风资源的分布情况．根据威布尔分布参数值能够计算出初步的发电量,进而为风电场的宏观选址和下一步测风塔的建立提供依据。     

Statistical wind direction modeling for the analysis of large scale wind power generation

Understanding the effects of large‐scale wind power generation on the electric power system is growing in importance as the amount of installed generation increases. In addition to wind speed, the direction of the wind is important when considering wind farms, as the aggregate generation of the farm depends on the direction of the wind. This paper introduces the wrapped Gaussian vector autoregressive process for the statistical modeling of wind directions in multiple locations. The model is estimated using measured wind direction data from Finland. The presented methodology can be used to model new locations without wind direction measurements. This capability is tested with two locations that were left out of the estimation procedure. Through long‐term Monte Carlo simulations, the methodology is used to analyze two large‐scale wind power scenarios with different geographical distributions of installed generation. Wind generation data are simulated for each wind farm using wind direction and wind speed simulations and technical wind farm information. It is shown that, compared with only using wind speed data in simulations, the inclusion of simulated wind directions enables a more detailed analysis of the aggregate wind generation probability distribution. Copyright © 2016 John Wiley & Sons, Ltd.     

基于改进粒子群优化算法与风速分布回归函数法的复杂地形风电场优化布置方法

为了研究在复杂地形下的风力机优化排布方法,提出一种改进粒子群（PSO）优化方法,并借助风速回归函数解决一部分复杂地形所致的问题,对实际尾流效应设置约束条件,判断风能利用的最优方案,从而快速确定风力机具体安放坐标,通过Matlab建模仿真,并借助WAsP软件对改进PSO优化算法和传统方法进行对比验证。结果表明,改进粒子群（PSO）优化方法与传统方法相比,年发电量提高了近5.2%,且对复杂地形下的风电场优化布局效果较好。     

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.     

Modelling and measurements of power losses and turbulence intensity in wind turbine wakes at Middelgrunden offshore wind farm

Understanding of power losses and turbulence increase due to wind turbine wake interactions in large offshore wind farms is crucial to optimizing wind farm design. Power losses and turbulence increase due to wakes are quantified based on observations from Middelgrunden and state‐of‐the‐art models. Observed power losses due solely to wakes are approximately 10% on average. These are relatively high for a single line of wind turbines due in part to the close spacing of the wind farm. The wind farm model Wind Analysis and Application Program (WAsP) is shown to capture wake losses despite operating beyond its specifications for turbine spacing. The paper describes two methods of estimating turbulence intensity: one based on the mean and standard deviation (SD) of wind speed from the nacelle anemometer, the other from mean power output and its SD. Observations from the nacelle anemometer indicate turbulence intensity which is around 9% higher in absolute terms than those derived from the power measurements. For comparison, turbulence intensity is also derived from wind speed and SD from a meteorological mast at the same site prior to wind farm construction. Despite differences in the measurement height and period, overall agreement is better between the turbulence intensity derived from power measurements and the meteorological mast than with those derived from data from the nacelle anemometers. The turbulence in wind farm model indicates turbulence increase of the order 20% in absolute terms for flow directly along the row which is in good agreement with the observations. Copyright © 2007 John Wiley & Sons, Ltd.     

大型风电机组功率曲线的分析与修正

讨论了风电机组不同情况下的功率曲线定义,分析了功率曲线绘制过程中的风速处理方法,可以适用于绘制风力发电机组静、动态功率曲线;讨论了影响机组功率曲线的各种因素,并给出了影响因子,使得根据功率曲线进行风场发电量的计算可以取得更可靠的结果。     

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.     

Modeling turbine wakes and power losses within a wind farm using LES: An application to the Horns Rev offshore wind farm

A modeling framework is proposed and validated to simulate turbine wakes and associated power losses in wind farms. It combines the large-eddy simulation (LES) technique with blade element theory and a turbine-model-specific relationship between shaft torque and rotational speed. In the LES, the turbulent subgrid-scale stresses are parameterized with a tuning-free Lagrangian scale-dependent dynamic model. The turbine-induced forces and turbine-generated power are modeled using a recently developed actuator-disk model with rotation (ADM-R), which adopts blade element theory to calculate the lift and drag forces (that produce thrust, rotor shaft torque and power) based on the local simulated flow and the blade characteristics. In order to predict simultaneously the turbine angular velocity and the turbine-induced forces (and thus the power output), a new iterative dynamic procedure is developed to couple the ADM-R turbine model with a relationship between shaft torque and rotational speed. This relationship, which is unique for a given turbine model and independent of the inflow condition, is derived from simulations of a stand-alone wind turbine in conditions for which the thrust coefficient can be validated. Comparison with observed power data from the Horns Rev wind farm shows that better power predictions are obtained with the dynamic ADM-R than with the standard ADM, which assumes a uniform thrust distribution and ignores the torque effect on the turbine wakes and rotor power. The results are also compared with the power predictions obtained using two commercial wind-farm design tools (WindSim and WAsP). These models are found to underestimate the power output compared with the results from the proposed LES framework.