双参数威布尔分布函数的确定及曲线拟合

双参数威布尔分布函数能准确地描述风速的实际分布。通过威布尔分布函数实际数学模型的建立,利用计算机软件（MATLAB）对其函数模型进行曲线拟合,并将拟合曲线应用到实际中,对风资源做初步评价。     

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。     

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。     

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.     

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。     

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     

风力—柴油系统仿真中的实时风速模拟研究

本言语运用风速概率密度函数（威布尔分布）由逆分布函数法推导出实时风速的数学模型，并对所产生的实时大小的随机点做了χ＾２检验，证明此方法是正确的。     

风力发电随机风速时间序列生成方法分析与评价

本文介绍了分别基于威布尔分布模型、组合风速模型和风轮等效风速模型的风力发电随机风速时间序列生成方法,给出了3种随机风速生成方法的详细过程并计算了给定参数下随机风速时间序列结果。为了对3种不同方法所获得的随机风速时间序列数据进行评价,采用风功率谱密度分析技术对随机风速时间序列进行分析,分析结果说明风轮等效风速模型不仅能够较为准确地体现自然界风速功率谱的分布,而且还能体现三叶片风电机组旋转作用的等效效果,在研究风电场电能质量以及并网运行方式对电网影响的场合具有很好的适用性。     

基于三适应度粒子群算法的风速威布尔分布参数估计

在传统粒子群算法的基础上,提出具有三适应度的粒子群算法来确定风速威布尔分布参数,其中适应度的函数分别根据风速拟合的相关程度、平均风能密度相对误差和方差3个指标进行定义。该方法具有收敛速度快综合考虑多方面指标的优点,能够确定对实际风频具有很好拟合效果的威布尔曲线参数,达到三适应度指标均最优的效果。利用三适应度粒子群算法对实例进行分析并与其他算法对比,结果表明该算法可较好估计威布尔参数,综合性能优点明显。     

对福建省陆地风能资源的评估

对我国福建省福州和厦门2座城市进行了风能开发潜力的评估.基于对该地区近15年的日平均风速的统计分析,计算了各月的风能密度,拟合出了Weibull分布密度函数的特征参数.用Weibull分布密度函数预测了各月的风能密度.并与实测值进行了对比及相关性分析,结果证明了Weibull函数对实测数据有很好的拟合性,同时也表明福建省陆地风力资源的不足,对该地区风力资源的调查重点应放在沿海滩涂及浅海.     

Wind power potential and characteristic analysis of the Pearl River Delta region, China

Wind power potentials of the Pearl River Delta (PRD) region have been statistically analyzed based on the hourly measured wind speed data in four islands. The hourly and monthly wind speed and wind power density are assessed to have remarkable variations, and the Weibull distribution function has been derived from the available data with its two parameters identified. The wind power and operating possibilities of these locations have been studied based on the Weibull function. The wind power potentials of these sites were found to be encouraging; however, the wind power at different site varies significantly, so attention should be paid to the wind conditions as well as the site terrains in choosing the wind farm sites.     

An Assessment of Wind Turbine Characteristics and Wind Energy Characteristics for Electricity Production

This work is an analysis of wind turbine characteristics and wind energy characteristics of four regions around Elazig, Turkey, namely Maden, Agin Elazig and Keban. Wind speed data and wind direction in measured hourly time-series format is statistically analyzed based on 6 years between 1998 and 2003. The probability density distributions are derived from time-series data and distributional parameters are identified. Two probability density functions are fitted to the measured probability distributions. The wind energy characteristic of all the regions is studied based on the Weibull and the Rayleigh distributions. Using the Weibull probability density function, we estimated the wind energy output and the capacity factor for six different wind turbines between 300 and 2300 kW during the six years. It was found that Maden is the best region, among the regions analyzed, for wind energy characteristic and wind turbine characteristic.     

Wind speed generation for dynamic analysis

A simple model to generate large band wind speed time sequences, especially easy to implement with a very reduced number of parameters, is presented. It is based on the calculation of a low‐frequency and a high‐frequency components. Low‐frequency component with 1 h sample time is obtained from a random process based on a conditional probability density function. Using real data from two different wind farms in two different months of the year, it has been found that Weibull distribution centered in the current hourly mean value seems to represent well the 1 h conditional PDF in all cases, and the standard deviation of this conditional Weibull is more or less in the range 1–1.3 m s^?1 independently of the season of the year or the location. Regarding to high‐frequency component, low‐frequency samples are used as initial and final values and, between them, the turbulence component values are inserted. For that, it has been used a stochastic process based on a Beta probability function and a simple rescaling procedure with two non‐linear parameters, calculated in a recursive way. Unlike the usual modelling procedures presented in the literature, spectral power density functions are not used. This simplifies the implementation significantly. Ten second sample‐time real speed wind data from two different wind farms have been used to validate the proposed high‐frequency model, obtaining excellent results. A thorough revision of the main models found in the literature to produce wind speed time sequences for dynamic analysis is performed in the paper. Copyright © 2017 John Wiley & Sons, Ltd.     

风切变指数在风电场风资源评估中的应用

以内蒙古地区3座70m高测风塔连续2年的实测数据来分析风切变指数的变化,结果表明:1)不同高度梯度的风切变指数受地面粗糙度及周围地形地貌的影响较大。2)计算相邻高度的风速时,采用相邻高度间的风切变指数计算得到的结果较好;计算相差较大的高度间风速时,采用拟合曲线得到的风切变指数计算得到的结果较好。3)利用3～25m/s的风切变指数计算各月风速及年均风速结果都与实测值最接近;而利用全部风速数据的风切变指数计算统计各月风速往往比实测值偏大;利用3～25m/s拟合曲线得到的风切变指数统计各月风速比实测值偏小。     

Wind speed time series characterization by Hilbert transform

Predictions of wind energy potential in a given region are based on on‐location observations. The time series of these observations would later be analysed and modelled either by a probability density function (pdf) such as a Weibull curve to represent the data or recently by soft computing techniques, such as neural networks (NNs). In this paper, discrete Hilbert transform has been applied to characterize the wind sample data measured on ?zmir Institute of Technology campus area which is located in Urla, ?zmir, Turkey, in March 2001 and 2002. By applying discrete Hilbert transform filter, the instantaneous amplitude, phase and frequency are found, and characterization of wind speed is accomplished. Authors have also tried to estimate the hourly wind data using daily sequence by Hilbert transform technique. Results are varying. Copyright © 2005 John Wiley & Sons, Ltd.     

Assessment of wind characteristics and wind turbine characteristics in Taiwan

Wind characteristics and wind turbine characteristics in Taiwan have been thoughtfully analyzed based on a long-term measured data source (1961–1999) of hourly mean wind speed at 25 meteorological stations across Taiwan. A two-stage procedure for estimating wind resource is proposed. The yearly wind speed distribution and wind power density for the entire Taiwan is firstly evaluated to provide annually spatial mean information of wind energy potential. A mathematical formulation using a two-parameter Weibull wind speed distribution is further established to estimate the wind energy generated by an ideal turbine and the monthly actual wind energy generated by a wind turbine operated at cubic relation of power between cut-in and rated wind speed and constant power between rated and cut-out wind speed. Three types of wind turbine characteristics (the availability factor, the capacity factor and the wind turbine efficiency) are emphasized. The monthly wind characteristics and monthly wind turbine characteristics for four meteorological stations with high winds are investigated and compared with each other as well. The results show the general availability of wind energy potential across Taiwan.