用耿贝尔模型统计分析极大风速出现概率

本文尝试利用气象站积累的长时间最大风速资料和有限的极大风速资料,结合大气边界层的一般规律,应用耿贝尔分布函数分析统计该地某高度极大风出现的概率.     

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.     

基于风电场短期测风数据50年一遇最大风速的算法探讨

针对目前国内多数风电场建设中气象站统计资料短缺,而风电场实际测风时间较短的现状,基于传统极值理论,本文从本质上探讨风电场50年一遇最大风速的算法,用Gumbell分布的极值理论肯定了独立暴风法的样本筛取方法及应用。通过风电场短期测风数据计算50年一遇最大风速,为风机选型、极限载荷的计算提供参考依据。     

五十年一遇最大风速计算方法比较

本文介绍了在重现期最大风速计算时针对长年代序列的Weibull(韦布尔)分布、Gumbel(龚贝尔、极值I型)分布、皮尔逊III型分布,适应短期序列(15-20年)风暴大风的Poisson—Gumbel复合分布,以及适合超短期序列(1-2年)二项—对数正态分布的计算方法,并用受热带气旋影响明显的香港横澜岛气象站1973-2011年最大风速资料、1992-2011年热带气旋大风资料和1999年、2008年、2010年完整年风速资料,进行了实例计算分析。结果表明,在长序列计算中,拟合优度检验显示Weibull分布计算结果比Gumbel分布及皮尔逊III型分布结果更合理。Poisson—Gumbel复合分布计算结果略小于长序列计算结果。三个完整年计算的50年一遇最大风速差别较大,因此,在进行50年一遇最大风速计算中要选择适当的计算方法,使计算结果稳定、可靠。     

样本选取对于风电场风资源评估的影响研究

以山东省某两个风电场作为计算实例,对于逐小时风速、整点前10 min风速、逐10 min风速作为取样样本对风资源评估的影响进行了研究。研究表明,三者计算得到的风资源基本一致,而且前两种样本取样方法与气象站的相关性也基本一致。笔者建议,在进行风资源评估时采用《风电场风能资源评估方法》(GB/T 18710-2002)中规定的逐小时风速作为计算依据。     

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。     

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。     

15°~35°N、105°~130°E海域台风极值风速分析

采用非对称台风风场模型,对1961~2007年进入(15°~35°N,105°~130°E)的每个热带气旋(TC)风场进行计算,得到0.5°×0.5°网格上的TC风速。使用近岸自动气象站实测资料对计算结果进行对比分析,结果表明计算风速与实测风速较吻合,计算风速基本合理。采用Poisson-Gumbel联合概率分布模型,计算每个网格TC影响下不同重现期10 m高度的极值风速;使用海上、小岛共10座测风塔14个台风影响过程的大风观测数据计算得到10~90 m切变系数为0.0791,使用该关系式将各网格点TC影响下不同重现期10 m高度的极值风速推算到90 m高度,可作为中国近海风资源开发和其他海洋工程开发及安全运行的科学依据。     

基于热稳定度风向标准差法的风速外推模型研究

针对风能评估中外推轮毂高度处风速的问题,分析了高层风切变指数特性,建立了基于风向标准差法的风速外推模型。该模型首先剔除小于3 m/s的低风速数据及大于25 m/s的高风速数据,再利用风向标准差法对该地区风资源进行热稳定度分类,计算相应的风切变指数,并进行轮毂高度处风速推算。计算结果表明,相对于没有进行数据筛选的计算模型,推算精度提高了5.46%,加强了风能评估的准确性,对风电场的风资源评估具有一定的指导意义。     

风电场平均风速变化对利用小时数的影响研究

本文分别采用两种方法,对风电场实测数据进行处理,得到平均风速依次递增的风速数据集序列,并提出一种结合数据集Weibull分布拟合结果及风电机组功率曲线数据,计算其对应发电量的数值积分方法;然后,本文结合3个风电场的实测风速数据,采用上述数据集处理和发电量计算方法,对其不同高度处的等效利用小时数进行了计算和对比分析研究,并与现有软件计算结果进行了对比验证。研究结果表明,本文提出的发电量计算方法可靠性较高,且平均风速每增加0.1m/s,等效利用小时数大致增加50h～60h。此研究结果也可为风能资源数据不同订正方法所造成的计算利用小时数变化规律的研究提供重要依据。     

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.     

Prediction of the extreme wind speed in the mixed climate region by using Monte Carlo simulation and measure‐correlate‐predict method

The extreme wind speed at an offshore location was predicted using Monte Carlo simulation (MCS) and measure‐correlate‐predict (MCP) method. The Gumbel distribution could successfully express the annual maximum wind speed of extratropical cyclone. On the other hand, the estimated extreme wind speed of tropical cyclones by analytical probability distribution shows larger uncertainty. In the mixed climate like Japan, the extreme wind speed estimated from the combined probability distribution obtained by MCP and MCS methods agrees well with the observed data as compared with the combined probability distribution obtained by the MCP method only. The uncertainty of extreme wind speed due to limited observation period of wind speed and pressure was also evaluated by the Gumbel theory and Monte Carlo simulation. As a result, it was found that the uncertainty of 50 year recurrence wind speed obtained by MCS method is considerably smaller than that obtained by MCP method in the mixed climate. Copyright © 2014 John Wiley & Sons, Ltd.     

风场风力条件评估系统开发

运用VC＋＋编写风场风力条件的评估系统．该系统包括正常风力条件和极端风力条件2大部分。在各个模块中针对不同的风力条件,采用交互式数据输入方式,计算出具体数值,且绘出图形,从而全面、具体地对风场风力条件进行分析与评估。     

考虑风资源影响的风电场电压波动和闪变评估

风电并网后引起的电压波动和闪变水平可能超出国家有关标准,造成严重的电能质量问题,因此,在风电并网之前需对这两者进行评估。采用了一种新的评估方法。区别于国际电工标准(IEC61400-21)中电压波动与闪变的评估,此方法考虑了风电场的风资源情况对这2个指标的影响。对风电场在不同出力下由阵风引起系统的电压波动进行计算,并用IEC闪变仪计算短时间闪变值Pst。用所提方法和IEC标准对我国某一新建的风电场进行评估。结果表明,所提方法不仅能有效地进行电压波动与闪变评估,而且能更好地考虑风速变化对风电场带来的潜在影响。     

风力机的新型变桨距自抗扰控制系统

采用高速浮点 TMS320F28335(DSP)芯片作为硬件核心控制器,运用自抗扰控制系统算法对风力机桨距角进行精确调整,实现了一种由新型软硬件相结合的风力机变桨距控制系统.结果表明:当风速高于额定风速或者瞬时输出功率大于额定功率时,通过对桨叶桨距角的精确调整,可使输出功率动态维持在额定功率附近.变桨距自抗扰控制系统算法实现简单,具有良好的动态响应特性,能有效保障风力机的安全运行.     

Copula-based joint distribution analysis of wind speed and wind direction: Wind energy development for Hong Kong

Accurate and reliable assessment of wind energy potential has important implication to the wind energy industry. Most previous studies on wind energy assessment focused solely on wind speed, whereas the dependence of wind energy on wind direction was much less considered and documented. In this paper, a copula-based method is proposed to better characterize the direction-related wind energy potential at six typical sites in Hong Kong. The joint probability density function (JPDF) of wind speed and wind direction is constructed by a series of copula models. It shows that Frank copula has the best performance to fit the JPDF at hilltop and offshore sites while Gumbel copula outperforms other models at urban sites. The derived JPDFs are applied to estimate the direction-related wind power density at the considered sites. The obtained maximum direction-related wind energy density varies from 41.3 W/m² at an urban site to 507.9 W/m² at a hilltop site. These outcomes are expected to facilitate accurate micro-site selection of wind turbines, thereby improving the economic benefits of wind farms in Hong Kong. Meanwhile, the developed copula-based method provides useful references for further investigations regarding direction-related wind energy assessments at various terrain regions. Notably, the proposed copula-based method can also be applied to characterize the direction-related wind energy potential somewhere other than Hong Kong.     

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.     

Response load extrapolation for wind turbines during operation based on average conditional exceedance rates

The paper explores a recently developed method for statistical response load (load effect) extrapolation for application to extreme response of wind turbines during operation. The extrapolation method is based on average conditional exceedance rates and is in the present implementation restricted to cases where the Gumbel distribution is the appropriate asymptotic extreme value distribution. However, two extra parameters are introduced by which a more general and flexible class of extreme value distributions is obtained with the Gumbel distribution as a subclass. The general method is implemented within a hierarchical model where the variables that influence the loading are divided into ergodic variables and time‐invariant non‐ergodic variables. The presented method for statistical response load extrapolation was compared with the existing methods based on peak extrapolation for the blade out‐of‐plane bending moment and the tower mudline bending moment of a pitch‐controlled wind turbine. In general, the results show that the method based on average conditional exceedance rates predicts the extrapolated characteristic response loads at the individual mean wind speeds well and results in more consistent estimates than the methods based on peak extrapolation. Copyright © 2011 John Wiley & Sons, Ltd.     

中美基本风速的换算

由于中美规范间基本风速的确定采用不同的时距,导致基本风速不同。本文提供了一个基本风速换算方法,以应用于不同规范。