逐时风速概率分布研究

风速概率分布及其参数是体现风能资源统计特性的最重要指标之一。以山东省4个风电场测风塔和气象站测风年的逐时风速为样本,采用正态分布、指数分布、威布尔分布、伽马分布和Logistics分布对逐时风速概率分布进行研究,以Akaike信息准则判断概率分布的适用性。研究结果表明,威布尔分布、伽马分布和Logistics分布能更好的拟合小时风速的实际情况。     

山东省风能调研方法的研究

本文分析比较了估算风能的几种方法,讨论了用10分钟平均风速代替1小时平均风速估计风能参数的可行性。1982—1983年山东省风能资源的调研中采用了此法。     

风工况双参数威布尔分布k值影响研究

在风能资源评估过程中,用于描述连续时限内风速概率分布的风速分布模型参数是重要的参考依据。目前,国际上广泛应用的风速概率分布模型为双参数威布尔分布,而风机生产商对于风机各项指标的设计又以形状参数等于2的风速威布尔分布(即瑞利分布)为依据。在我国风资源气象条件的实     

基于威布尔分布的风速概率分布参数估计方法

准确地描述风速特性,直接影响风电场风能资源评估的结果.文章介绍了基于威布尔分布的平均风速及最大风速估计法、矩估计法、最小二乘估计法和极大似然估计法等4种风速概率分布参数估计的方法.通过对乌兰察布地区测风塔实际数据的分析,比较了4种方法的参数估计结果,得到以下结论:在风能资源较丰富地区,平均风速及最大风速估计法的风速拟合效果波动较大,对平均风能密度估计误差较大;矩估计法、最小二乘估计法和极大似然估计法拟合效果良好.     

都昌老爷庙风电场风能资源研究

利用都昌气象局对老爷庙风电场所测的数据,对其风能资源中平均风速、平均风能密度、有效风能密度、有效时数等参数进行了详细的计算和分析.利用威布尔双参数曲线拟合风的频率曲线, 对其两个参数k和c的估算用三种不同方法分别加以探讨并进行误差对比,最终得到老爷庙风电场的风能评估结果.     

基于威布尔分布的风电场微观选址优化

提出基于风能全年概率分布特征的风电场微观选址优化方法.采用威布尔分布和风向玫瑰图描述风能的全年变化特性,其中威布尔分布的尺度参数和形状参数根据风速统计数据利用最小二乘法获得.以风电场单位功率发电成本最小化为目标函数,运用遗传算法进行微观选址问题的优化求解.仿真结果表明微观选址优化方案反映了风能的全年变化特性,实现了单位...     

中国风速概率分布及在风能评估中的应用

运用Weibull分布模型研究了我国日平均风速的概率分布、平均风能密度以及风能可利用时数,并比较了此模型与正态分布拟合风速概率分布的区别。结果表明:Weibull分布比正态分布能更好地表示出风速概率分布的偏度和峰度。我国平均风能密度春季最大,夏季最小。山东半岛、闽浙沿海及岛屿地带的平均风能密度最大;新疆和甘肃的北部地区、内蒙古中东部和东北地区次之;塔里木盆地、四川盆地、云贵高原以及两广的内陆地区的平均风能密度最小。风能可利用时数与平均风能密度的地理分布和季节差异定性上一致。     

基于长期实测资料的风速测量相关推测方法对比

针对海上风电的风能资源评估,利用4个沿海自动气象站2003—2010年的逐时测风数据与同期MERRA再分析资料,通过6组数值试验,从平均风速、风速标准差、平均风功率密度和风力发电机组的年发电量等4个评价指标全面对比分析了8种风资源测量-相关-推测方法。结果表明:简单正交回归法、简化主轴回归法、分位数回归法、Weibull尺度拟合法和矩阵时间序列法对平均风速的推测结果更优,简单正交回归法、简化主轴回归法和Weibull尺度拟合法对风速标准差和平均风功率密度的推测结果更优,联合概率分布法对风向-风速联合频率分布和风机发电量的推测结果明显优于其他方法。参证站自身的数据质量和空间代表性对测量-相关-推测方法的表现具有潜在影响,在实际工作中应对多种测量-相关-推测方法进行集成运用和综合判断,以符合海上风电多层次、多目标的风资源评估需求。     

新疆达坂城风电场风能资源特性分析

对新疆达坂城风电场的风能资源特性进行了详细的研究。基于在达坂城风电场实测的10m和24m高程的10min平均风速数据,分析了原始风速的分布特性。根据地表风速沿高度呈风剪指数分布的特性,计算了在各个轮毂高度上的风速分布。采用最小误差逼近算法原理,计算了风速韦布尔分布的参数以及平均风速和分布方差。通过对韦布尔分布的分析,计算了各个高度上风电场的平均风功率密度、有效平均风功率密度和可利用小时数等风能资源特性参数,为当地的风能开发提供分析基础。     

基于GIS的江苏省陆地风能资源潜力评估及微观选址

基于江苏省14个气象站地面观测资料,从有效风能密度、有效总时数及威布尔二参数模型对研究区的风能潜力进行了分析,同时选用Vestas V80风机作为参考装机,结合研究区土地利用现状,排除不适宜风能开发利用的区域,估算研究区可装机潜力。结果表明:江苏省境内风能资源呈东高西低分布,越靠近海边风能资源越丰富;根据风能区划标准,全省风能资源丰富区主要位于东部沿海的南通、盐城等地区,较为丰富地区分布于苏北的连云港及苏南部分地区,相对较为贫乏的地区是内陆的镇江、扬州、常州等地区;全省80m高度四季平均风速均在可利用范围(3m/s以上),其中东部沿海地区四季风速均在4～5m/s,有较大的可开发利用潜力;据估算全省适宜风电开发的区域面积大约是18133.4km~2,可安装4万多座Vestas V80风机,年均发电量约为1.46×10~5MkWh。     

The influence of the Weibull assumption in monthly wind energy estimation

An estimation of the monthly wind energy output for the period 1999–2003 at five wind farms in northeastern Spain was evaluated. The methodology involved the calculation of wind speed histograms and the observed average wind power versus wind relation obtained from hourly data. The energy estimation was based on the cumulated contribution of the wind power from each wind speed interval. The impact of the Weibull distribution assumption as a substitute of the actual histogram in the wind energy estimation was evaluated. Results reveal that the use of a Weibull probability distribution has a moderate impact in the energy calculation as the largest estimation errors are, on average, no larger than 10% of the total monthly energy produced. However, the evaluation of the goodness of fit through the χ² statistics shows that the Weibull assumption is not strictly substantiated for most of the sites. This apparent discrepancy is based on the partial cancellation of the positive and negative departures of the Weibull fitted and the actual wind frequency distributions. Further investigation of the relation between the χ² and the error contribution exposes a tendency of the Weibull distribution to underestimate (overestimate) the observed histograms in the lower and upper (intermediate) wind speed intervals. This fact, together with the larger wind power weight over the highest winds, results in a systematic total wind energy underestimation. Copyright © 2008 John Wiley & Sons, Ltd.     

陕西省金润河北镇风电场风能资源开发评估

对陕西省宝鸡市陇县金润河北镇风电场气象条件、风功率密度、平均风速、主导风向等风能参数进行分析评价。结果表明,测风塔100 m高度月平均风速、月平均风功率密度最大均出现在4月,最小均出现在8月;测风塔100 m高度主导风向为SSW(南西南),主要风能方向为SSW(南西南),风电场风功率密度等级为1级。风电场安装20台2500 kW的风电机组,装机容量50 MW,年设计发电量1.33485×108 kW·h,年出厂电量9.5426×107 kW·h。结果可为其他风电场选址和发电量估算提供参考。     

基于PWMM的三参数Weibull分布参数外推方法的研究

为提高低风速区分散式风电项目的风资源评估精度,降低测风成本,在对三参数Weibull分布参数估计和外推的研究基础上,提出基于概率加权矩法(PWMM)的三参数Weibull分布参数垂直外推方法。利用较低高度处风速统计的概率加权矩,经垂直外推得到平坦地形、较高高度处风速Weibull分布的参数,进而得到Weibull分布函数和风功率密度。算例分析表明:基于PWMM的三参数Weibull分布参数垂直外推法在平坦地形不同测风点处有一定的适用性外推较高高度处风速Weibull分布的参数,可有效体现平坦地形低风速区的风速分布特征,提高风功率密度评测精度。     

Assessing the suitability of wind speed probabilty distribution functions based on wind power density

Three functions have so far predominantly been used for fitting the measured wind speed probability distribution in a given location over a certain period of time, typically monthly or yearly. In the literature, it is common to fit these functions to compare which one fits the measured distribution best in a particular location. During this comparison process, parameters on which the suitability of the fit is judged are required. The parameters that are mostly used are the mean wind speed or the total wind energy output (primary parameters). It is, however, shown in the present study that one cannot judge the suitability of the functions based on the primary parameters alone. Additional parameters (secondary parameters) that complete the primary parameters are required to have a complete assessment of the fit, such as the discrepancy between the measured and fitted distributions, both for the wind speed and wind energy (that is the standard deviation of wind speed and wind energy distributions). Therefore, the secondary statistical parameters have to be known as well as the primary ones to make a judgement about the suitability of the distribution functions analysed. The primary and secondary parameters are calculated from the 12-month of measured hourly wind speed data and detailed analyses of wind speed distributions are undertaken in the present article.     

Designing an index for assessing wind energy potential

To meet the increasing global demand for renewable energy, such as wind energy, an increasing number of wind parks are being constructed worldwide. Finding a suitable location requires a detailed and often costly analysis of local wind conditions. Plain average wind speed maps cannot provide a precise forecast of wind power because of the non-linear relationship between wind speed and production. We suggest a new approach to assess the local wind energy potential. First, meteorological reanalysis data are applied to obtain long-term low-scale wind speed data at specific turbine locations and hub heights. Second, the relation between wind data and energy production is determined via a five parameter logistic function using actual high-frequency energy production data. The resulting wind energy index allows for a turbine-specific estimation of the expected wind power at an unobserved location. A map of the wind power potential for Germany exemplifies our approach.     

Determination of Weibull parameters for wind energy analysis of İzmir,Turkey

In this study, the two Weibull parameters of the wind speed distribution function, the shape parameter k (dimensionless) and the scale parameter c (ms^?1), were computed from the wind speed data for ?zmir. Wind data, consisting of hourly wind speed records over a 5‐year period, 1995–1999, were measured in the Solar/Wind‐Meteorological Station of the Solar Energy Institute at Ege University. Based on the experimental data, it was found that the numerical values of both Weibull parameters (k and c) for ?zmir vary over a wide range. The yearly values of k range from 1.378 to 1.634 with a mean value of 1.552, while those of c are in the range of 2.956–3.444 with a mean value of 3.222. The average seasonal Weibull distributions for ?zmir are also given. The wind speed distributions are represented by Weibull distribution and also by Rayleigh distribution, with a special case of the Weibull distribution for k=2. As a result, the Weibull distribution is found to be suitable to represent the actual probability of wind speed data for ?zmir (at annual average wind speeds up to 3 ms^?1). Copyright © 2002 John Wiley & Sons, Ltd.     

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。