Article(id=1154429759049163633, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1154429758340326256, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1664467200000, receivedDateStr=2022-09-30, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1753167102824, onlineDateStr=2025-07-22, pubDate=1718812800000, pubDateStr=2024-06-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1753167102824, onlineIssueDateStr=2025-07-22, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1753167102824, creator=13701087609, updateTime=1753167102824, updator=13701087609, issue=Issue{id=1154429758340326256, tenantId=1146029695717560320, journalId=1146119893612605453, year='2024', volume='42', issue='6', pageStart='710', pageEnd='852', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1753167102656, creator=13701087609, updateTime=1753694592332, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1156642210440401438, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1154429758340326256, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1156642210440401439, tenantId=1146029695717560320, journalId=1146119893612605453, issueId=1154429758340326256, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=812, endPage=819, ext={EN=ArticleExt(id=1154429759506342772, articleId=1154429759049163633, tenantId=1146029695717560320, journalId=1146119893612605453, language=EN, title=Simulation of wind field in micro-grid considering wind direction correlation, columnId=null, journalTitle=Renewable Energy Resources, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In order to calculate the time history response of wind turbine structure more accurately, the influence of wind shear, tower shadow and mountain environment was considered. Based on Kaimal selfpower spectrum and crosspower spectrum, the harmonic superposition method was used to simulate fluctuating wind speed. The spatial three dimensional non stationary wind field simulation method with wind direction correlation was established by using turbulence coherence model. Taking 2 MW wind turbine of a wind farm as an example, the correctness of the simulation method is verified and compared with the simulation results without considering the correlation of wind direction. The results from statistical analysis of simulated data and comparison with target spectrum demonstrated the accuracy and accuracy of the presented method, the simulation of threedimensional nonstationary fluctuating wind field can be realized. The results from statistical analysis of simulated data and comparison with target spectrum demonstrated accuracy of the presented method, and considering the correlation of wind direction can improve the simulation coincidence, which proves the necessity of simulation method.

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为了更加准确地进行风电机组结构时程响应计算,考虑风剪切、塔影和山地环境等对平均风速的影响,文章基于 Kaimal 自功率谱和纵向与竖向脉动风速分量互功率谱,采用谐波叠加法通过湍流相干模型建立空间三维且具备风向相关性的非平稳风场。选取某风电场2MW风电机组为例进行实例分析,对模拟方法进行验证,并与不考虑风向相关性进行对比。模拟结果表明,风速时程数据特征符合预期,得到的模拟功率谱与目标谱吻合较好,考虑风向相关性可提高模拟吻合度。从而证明了模拟方法的准确性和必要性,实现了三维非平稳脉动风场的模拟。

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杨春侠(1972-),女,博士,副教授,研究方向为风机结构可靠度。E-mail:

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杨春侠(1972-),女,博士,副教授,研究方向为风机结构可靠度。E-mail:

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杨春侠(1972-),女,博士,副教授,研究方向为风机结构可靠度。E-mail:

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in all directions, figureFileSmall=null, figureFileBig=null, tableContent=
纵向风速
节点 模拟平均风速/m $\cdot {\mathrm{s}}^{-1}$ 理论平均风速 $/\mathrm{m} \cdot {\mathrm{s}}^{-1}$ 模拟最大速度/m·s ${}^{-1}$ 模拟最小速度/m·s ${}^{-1}$ 模拟湍流强度
98 10.328 7 10.292 1 14.020 5 7.493 9 0.120 6
111 10.072 7 10.0430 13.649 9 6.1356 0.1251
137 9.460 7 9.383 7 13.349 9 4.5335 0.1407
163 8.632 6 8.2836 12.968 3 3.9384 0.1752
横向风速
节点 模拟平均风速/m 理论平均风速 模拟最大速度/m·s 模拟最小速度/m·s 模拟湍流强度
98 -0.2117 0 3.6539 $- {4.064}\;1$ 0.0994
111 $- {0.201}\;3$ 0 3.3551 $- {3.329}\;8$ 0.1027
137 -0.1943 0 3.437 3 -2.7236 0.1079
163 $- {0.097}\;1$ 0 3.931 6 -3.6763 0.1238
竖向风速
节点 模拟平均风速/m 理论平均风速 模拟最大速度 模拟最小速度/m·s 模拟湍流强度
98 -0.0230 0 3.062 9 -2.5067 0.091 8
111 -0.0404 0 2.5050 -2.6538 0.093 5
137 $- {0.046}\;9$ 0 3.1074 $- {2.790}\;1$ 0.096 9
163 $- {0.085}\;5$ 0 2.7454 -3.8673 0.1027
), ArticleFig(id=1154429815475134959, tenantId=1146029695717560320, journalId=1146119893612605453, articleId=1154429759049163633, language=CN, label=表 1, caption=风电机组各向风速特征数据, figureFileSmall=null, figureFileBig=null, tableContent=
纵向风速
节点 模拟平均风速/m $\cdot {\mathrm{s}}^{-1}$ 理论平均风速 $/\mathrm{m} \cdot {\mathrm{s}}^{-1}$ 模拟最大速度/m·s ${}^{-1}$ 模拟最小速度/m·s ${}^{-1}$ 模拟湍流强度
98 10.328 7 10.292 1 14.020 5 7.493 9 0.120 6
111 10.072 7 10.0430 13.649 9 6.1356 0.1251
137 9.460 7 9.383 7 13.349 9 4.5335 0.1407
163 8.632 6 8.2836 12.968 3 3.9384 0.1752
横向风速
节点 模拟平均风速/m 理论平均风速 模拟最大速度/m·s 模拟最小速度/m·s 模拟湍流强度
98 -0.2117 0 3.6539 $- {4.064}\;1$ 0.0994
111 $- {0.201}\;3$ 0 3.3551 $- {3.329}\;8$ 0.1027
137 -0.1943 0 3.437 3 -2.7236 0.1079
163 $- {0.097}\;1$ 0 3.931 6 -3.6763 0.1238
竖向风速
节点 模拟平均风速/m 理论平均风速 模拟最大速度 模拟最小速度/m·s 模拟湍流强度
98 -0.0230 0 3.062 9 -2.5067 0.091 8
111 -0.0404 0 2.5050 -2.6538 0.093 5
137 $- {0.046}\;9$ 0 3.1074 $- {2.790}\;1$ 0.096 9
163 $- {0.085}\;5$ 0 2.7454 -3.8673 0.1027
), ArticleFig(id=1154429815538049524, tenantId=1146029695717560320, journalId=1146119893612605453, articleId=1154429759049163633, language=EN, label=Table 2, caption=Simulation error of each model, figureFileSmall=null, figureFileBig=null, tableContent=
节点 误差 类别 文献[ 2 ] 方法 文献[ 3 ] 方法 文献[ 4 ] 方法 本文 方法
98 MSE 0.001 5 0.0190 0.037 9 0.009 53
RMSE 0.0025 0.018 8 0.050 1 0.009 63
MAE 0.004 2 0.0695 0.051 7 0.007 77
111 MSE 0.001 5 0.061 8 0.091 5 0.006 84
RMSE 0.0025 0.0638 0.021 3 0.004 87
MAE 0.004 2 0.0420 0.069 2 0.003 22
137 MSE 0.001 5 0.094 0 0.098 9 0.007 15
RMSE 0.0025 0.084 3 0.081 9 0.001 43
MAE 0.004 2 0.092 4 0.039 6 0.008 23
163 MSE 0.001 5 0.0184 0.0298 0.008 20
RMSE 0.0025 0.092 6 0.010 7 0.003 16
MAE 0.004 2 0.078 5 0.0521 0.001 12
), ArticleFig(id=1154429815617741303, tenantId=1146029695717560320, journalId=1146119893612605453, articleId=1154429759049163633, language=CN, label=表 2, caption=各模型模拟误差, figureFileSmall=null, figureFileBig=null, tableContent=
节点 误差 类别 文献[ 2 ] 方法 文献[ 3 ] 方法 文献[ 4 ] 方法 本文 方法
98 MSE 0.001 5 0.0190 0.037 9 0.009 53
RMSE 0.0025 0.018 8 0.050 1 0.009 63
MAE 0.004 2 0.0695 0.051 7 0.007 77
111 MSE 0.001 5 0.061 8 0.091 5 0.006 84
RMSE 0.0025 0.0638 0.021 3 0.004 87
MAE 0.004 2 0.0420 0.069 2 0.003 22
137 MSE 0.001 5 0.094 0 0.098 9 0.007 15
RMSE 0.0025 0.084 3 0.081 9 0.001 43
MAE 0.004 2 0.092 4 0.039 6 0.008 23
163 MSE 0.001 5 0.0184 0.0298 0.008 20
RMSE 0.0025 0.092 6 0.010 7 0.003 16
MAE 0.004 2 0.078 5 0.0521 0.001 12
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考虑风向相关性的风电机组山地湍流风场模拟研究
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杨春侠 , 于增豪 , 朱陶炜
可再生能源 | 2024,42(6): 812-819
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可再生能源 | 2024, 42(6): 812-819
考虑风向相关性的风电机组山地湍流风场模拟研究
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杨春侠 , 于增豪, 朱陶炜
作者信息
  • 1 长沙理工大学 土木工程学院 湖南 长沙 410114
  • 杨春侠(1972-),女,博士,副教授,研究方向为风机结构可靠度。E-mail:

Simulation of wind field in micro-grid considering wind direction correlation
Chunxia Yang , Zenghao Yu, Taowei Zhu
Affiliations
  • 1 School of Civil Engineering Changsha University of Science & Technology Changsha 410114 China
出版时间: 2024-06-20
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为了更加准确地进行风电机组结构时程响应计算,考虑风剪切、塔影和山地环境等对平均风速的影响,文章基于 Kaimal 自功率谱和纵向与竖向脉动风速分量互功率谱,采用谐波叠加法通过湍流相干模型建立空间三维且具备风向相关性的非平稳风场。选取某风电场2MW风电机组为例进行实例分析,对模拟方法进行验证,并与不考虑风向相关性进行对比。模拟结果表明,风速时程数据特征符合预期,得到的模拟功率谱与目标谱吻合较好,考虑风向相关性可提高模拟吻合度。从而证明了模拟方法的准确性和必要性,实现了三维非平稳脉动风场的模拟。

风电机组  /  谐波叠加法  /  非平稳脉动风  /  风向相关性  /  功率谱检验

In order to calculate the time history response of wind turbine structure more accurately, the influence of wind shear, tower shadow and mountain environment was considered. Based on Kaimal selfpower spectrum and crosspower spectrum, the harmonic superposition method was used to simulate fluctuating wind speed. The spatial three dimensional non stationary wind field simulation method with wind direction correlation was established by using turbulence coherence model. Taking 2 MW wind turbine of a wind farm as an example, the correctness of the simulation method is verified and compared with the simulation results without considering the correlation of wind direction. The results from statistical analysis of simulated data and comparison with target spectrum demonstrated the accuracy and accuracy of the presented method, the simulation of threedimensional nonstationary fluctuating wind field can be realized. The results from statistical analysis of simulated data and comparison with target spectrum demonstrated accuracy of the presented method, and considering the correlation of wind direction can improve the simulation coincidence, which proves the necessity of simulation method.

wind turbine  /  harmonic superposition method  /  non-stationary fluctuating wind  /  wind direction correlation  /  power spectrum verification
杨春侠, 于增豪, 朱陶炜. 考虑风向相关性的风电机组山地湍流风场模拟研究. 可再生能源, 2024 , 42 (6) : 812 -819 .
Chunxia Yang, Zenghao Yu, Taowei Zhu. Simulation of wind field in micro-grid considering wind direction correlation[J]. Renewable Energy Resources, 2024 , 42 (6) : 812 -819 .
风力发电机组大多建立在山地较高的位置, 山地风具有风速高、非平稳性等特点 [ 1 ] ,同时风向之间具有相关性, 风速越大, 相关性越强。因此, 研究风电机组的动力特性时, 获得符合风电机组运行环境的脉动风场是重要工作之一。
目前,大多数学者选择时域分析方法模拟脉动风,其中谐波叠加法和线性滤波法应用最为广泛。线性滤波法计算过程较为繁琐, 并且模拟精度难以控制。而谐波叠加法简单直观数学基础严密, 模拟样本具有稳定性和目标谱收敛性, 因此在风工程领域得到广泛应用。文献[ 2 ]提出了改进的线性滤波自回归 AR 模型法, 利用迭代法求解回归系数,对大跨空间结构的风荷载进行了模拟。 文献[ 3 ]基于改进 Von Karman 模型,分别采用谐波叠加法和自回归滑动平均法对某 ${33}\mathrm{\;{kW}}$ 水平轴风力机来流风况进行空间各方向独立的脉动风速模拟。文献[ 4 ]考虑了空间相关性模拟海上风电机组风场,得到叶片与塔筒结构表面控制点顺风向风速时程曲线。
综上所述, 关于风电机组模拟过程中均假定 $x, y, z$ 3 个方向为独立的一维风速场,忽略了方向之间的相关性,与实际风场环境不符。因此,在已有的风电机组风场模拟基础上,开展考虑具有风向相关性的三维非平稳风电机组山地湍流风场模拟方法研究是十分必要的。
风电机组风场运动是关于时间和空间相关的各向非均匀随机过程, 一般把风场中风速分为平均风速和脉动风速两个部分, 前者可根据长期的统计资料进行计算, 后者是局部的湍流运动形成的, 可根据其功率谱函数进行脉动风速时程的数值模拟, 最后将两者迭加就可以得到总的风速。对于三维自然风场, 其中任意一点的风速在笛卡儿坐标系中为
$\left\{\begin{array}{l} U =\bar{U}\left( z\right)+ u\left({y, z, t}\right)\\ v = v\left({y, z, t}\right)\\ w = w\left({y, z, t}\right)\end{array}\right.$
式中: $U$ 为纵向来流风速; $\bar{U}$ 为纵向平均风速; $u, v$$w$ 分别为纵向、横向和竖向脉动风速分量; $t$ 为时间。
由于实际风电机组风场环境的特殊性, 风场的平均风速应该从以下方面进行相应修正。
风剪切是指在平均风速随离地面高度不断变化的情况。考虑风剪切时常用指数模型,即:
$ U\left( z\right)= U\left({z}_{\mathrm{h}}\right){\left(\frac{z}{{z}_{\mathrm{h}}}\right)}^{\alpha }$
式中: $U\left( z\right)$ 为离地高度 $z$ 处的风速; $z$ 为离地垂直高度; $U\left({z}_{\mathrm{h}}\right)$ 为轮毂处风速; ${z}_{\mathrm{h}}$ 为轮毂离地高度; $\alpha$ 为风廓线指数。
上游平坦地面的流风在山地地形的干扰下, 导致山地地面以上局部区域的风速会大于未受扰动的风速, 即产生一种加速效应。采用 “Taylor-Lee 算法”, 在文献[ 5 ]研究的基础上, 考虑山体坡度和高度对平均风速加速效应的影响,即:
${\Delta S}= \frac{U\left( z\right)- {U}_{0}\left( z\right)}{{U}_{0}\left( z\right)} \\\Delta {S}_{\max }= {\eta }_{B}\left\lbrack {{B}_{1}h/{L}_{1}+ {B}_{2}{\left( h/{L}_{1}\right)}^{2}}\right\rbrack \\{\Delta S}= \Delta {S}_{\max }\exp \left({-{\eta }_{A}{Az}/h}\right)$
式中: ${U}_{0}\left( z\right)$ 为平地地面以上 $z$ 高度处的风速; ${L}_{1}$ 为一侧山体宽度; $h$ 为山体高度; ${\eta }_{B},{\eta }_{A}, A,{B}_{1},{B}_{2}$ 为修正系数,其计算式和取值可参考文献[ 5 ]。
塔影效应是由于塔筒的存在对风场平均风速造成阻碍的作用, 使风场平均风速发生大小和方向的改变。考虑塔影的影响时常采用势流模型, 即:
$ U\left({x, z}\right)= C{U}_{0}\\ C = 1 +{\left(\frac{D}{2}\right)}^{2}\frac{\left({x}^{2}- {z}^{2}\right)}{{\left({x}^{2}+ {z}^{2}\right)}^{2}}\\ D = c{D}_{\mathrm{T}}$
式中: ${D}_{\mathrm{T}}$ 为开始考虑塔影效应高度处对应的塔筒直径; $c$ 为塔筒直径的修正系数; $z$ 为节点距离塔筒中心的纵向距离; $x$ 为风矢量距离塔筒中心的横向距离。
风电场由多个风电机组组成, 上游风电机组会对风速产生干扰,处于下游风电机组就必须考虑尾流效应的影响。
$ U ={U}_{0}\left({1}_{-\kappa {\mathrm{e}}^{-\frac{{r}^{2}}{2{\delta }^{2}}}}\right)$
式中: $r$ 为到尾流中心线的距离; $\kappa$ 为速度逆差; $\delta$ 为尾流宽度。
针对风电机组结构特点, 风场功率谱密度采用随高度变化的 Kaimal 自功率谱与互功率谱 [ 6 ] ,其表达式分别为
纵向谱:
$\frac{n{S}_{u}\left( f\right)}{{u}_{* }^{2}}= \frac{105f}{{\left( 1 +{33}f\right)}^{\frac{5}{3}}}$
横向谱:
$\frac{n{S}_{v}\left( f\right)}{{u}_{* }^{2}}= \frac{17f}{{\left( 1 +{9.5}f\right)}^{\frac{5}{3}}}$
竖向谱:
$\frac{n{S}_{w}\left( f\right)}{{u}_{* }^{2}}= \frac{2f}{{\left( 1 +{5.3}f\right)}^{\frac{5}{3}}}$
纵向与竖向互谱:
$\frac{-n{C}_{uv}\left( f\right)}{{u}_{* }^{2}}= \frac{14f}{{\left( 1 +{9.6}f\right)}^{2.4}}$
式中: $f =\frac{nz}{\bar{U}\left( z\right)}$ 为无量纲 Monin 坐标; $n$ 为频率; $z$ 为节点离地面的高度; ${u}_{* }= \frac{K\bar{U}\left( z\right)}{\ln \left({z/{z}_{0}}\right)}$ 为摩擦风速; $K =$ ${0.4};{z}_{0}$ 为地面粗糙长度。
自然风是一种典型的湍流运动, 由于湍流是复杂、多尺度的,不同空间位置处风速和相位都不相同,即风场先作用于结构较高处,经过时间差 $\tau$ 后, 作用于结构较低处, 导致在同一水平高度上两点, 因相位差的不同, 脉动风速时程序列存在相关性,因此互功率谱矩阵通常为复数形式。
对于空间 $n$ 个模拟节点,具有一维多变量的零均值平稳随机过程 ${u}_{i}\left( t\right)= {\left\lbrack \begin{array}{llll}{u}_{1}\left( t\right)& {u}_{2}\left( t\right)& \cdots &{u}_{n}\left( t\right)\end{array}\right\rbrack }^{\mathrm{T}}$ 的互谱密度矩阵为
$\mathbf{S}\left(\omega \right)= \left\lbrack \begin{matrix}{S}_{11}\left(\omega \right)& {S}_{12}\left(\omega \right)& \cdots &{S}_{1n}\left(\omega \right)\\{S}_{21}\left(\omega \right)& {S}_{22}\left(\omega \right)& \cdots &{S}_{2n}\left(\omega \right)\\\vdots &\vdots &\ddots &\vdots \\{S}_{n1}\left(\omega \right)& {S}_{n2}\left(\omega \right)& \cdots &{S}_{nn}\left(\omega \right)\end{matrix}\right\rbrack $
式中: ${S}_{ii}\left(\omega \right)$ 为自谱函数; ${S}_{ij}\left(\omega \right)$ 互谱函数,其表达式为 [ 7 ]
${S}_{ij}\left(\omega \right)= \sqrt{{S}_{ii}\left(\omega \right){S}_{jj}\left(\omega \right)}{\operatorname{Coh}}_{ij}\left(\omega \right)\exp \left\lbrack {{i\phi }\left(\omega \right)}\right\rbrack \left({11}\right)$
式中: ${\operatorname{Coh}}_{ij}\left(\omega \right)$ 为相干函数; $\phi \left(\omega \right)$ 为两个离散点之间的相位差,二者的关系为 [ 8 ]
$\phi \left(\omega \right)= \left\{\begin{matrix}{0.25\pi f}& f \leq {0.1}\\- {10\pi f}+ {1.25}& {0.1}\leq f \leq {0.125}\\\left\lbrack {-\pi ,\pi }\right\rbrack \text{ 随机数 }& f \geq {0.125}\end{matrix}\right.$
由式 (10) 可知, $f$$0 \sim {0.1}$ 时,相位角呈线性增加; $f$ 为 0.1 $\sim {0.125}$ 时,相位角呈线性减小; 当 $f >$ 0.125 时,相位角互不相关,并在 $-\pi \sim \pi$ 随机分布。
根据互功率谱密度矩阵性质,对 $\mathbf{S}\left(\omega \right)$ 进行 Cholesky 分解:
$\mathbf{S}\left(\omega \right)= \mathbf{H}\left(\omega \right)\cdot {\mathbf{H}}^{*\mathrm{\;T}}\left(\omega \right)\\\mathbf{H}\left(\omega \right)= \left\lbrack \begin{matrix}{H}_{11}\left(\omega \right)& 0 &\cdots & 0 \\{H}_{21}\left(\omega \right)& {H}_{22}\left(\omega \right)& \cdots & 0 \\\cdots &\cdots &\cdots &\cdots \\{H}_{11}\left(\omega \right)& {H}_{22}\left(\omega \right)& \cdots &{H}_{m}\left(\omega \right)\end{matrix}\right\rbrack $
式中: $\mathbf{H}\left(\omega \right)$ 为下三角矩阵; ${\mathbf{H}}^{\mathrm{{vT}}}\left(\omega \right)$$\mathbf{H}\left(\omega \right)$ 的共轭转置矩阵。
根据 Shinozuka 理论 [ 9 , 10 ] ,节点的脉动风速时程为
${u}_{i}\left( t\right)= \mathop{\sum }\limits_{{m = 1}}^{i}\mathop{\sum }\limits_{{k = 1}}^{N}\left|{{G}_{im}\left({\omega }_{k}\right)}\right|\sqrt{2\Delta \omega }.\\\cos \left\lbrack {{\omega }_{k}t -{\theta }_{im}\left({\omega }_{k}\right)+ {\Phi }_{mk}}\right\rbrack $
式中: $N$ 为频率等分数,即频率域内的数据采样数目; ${\Delta \omega }= {\omega }_{u}/N$ 为频率增量; ${\omega }_{u}$ 为截止频率,即当 $\omega >{\omega }_{u}$ 时,谱密度矩阵为零; ${\Phi }_{mk}$ 为均匀分布于 $\left\lbrack {0,{2\pi }}\right\rbrack$ 的独立相位角; ${\theta }_{im}\left(\omega \right)$${H}_{im}$ 的复角; ${\omega }_{k}$ 为双索引频率,其表达式为
${\theta }_{im}\left(\omega \right)= {\tan }^{-1}\left\{\frac{\operatorname{Im}\left\lbrack {{H}_{im}\left(\omega \right)}\right\rbrack }{\operatorname{Re}\left\lbrack {{H}_{im}\left(\omega \right)}\right.}\right\}$
式中: $\operatorname{Im}\left\lbrack \cdot \right\rbrack$$\operatorname{Re}\left\lbrack \cdot \right\rbrack$ 分别为 ${G}_{im}$ 的虚部和实部。
${\omega }_{k}= \left({k - 1}\right){\Delta \omega }+ \frac{m}{n}{\Delta \omega }$
为了保证模拟结果的真实性, 模拟时程时间间隔 ${\Delta t}$ 必须满足 ${\Delta t}\leq \pi /{\omega }_{u}$ 。模拟过程的周期 ${T}_{0}$ 表达式为
${T}_{0}= \frac{2\pi nN}{{\omega }_{u}}$
同时,在模拟风速时考虑风场 $x$$z$ 方向的相关性,即考虑脉动分量 $u$$w$ 的相干。因此互谱功率谱密度函数矩阵改为
$\mathbf{S}\left(\omega \right)= \left\lbrack \begin{matrix}{S}_{uu}\left(\omega \right)& 0 &{S}_{uw}\left(\omega \right)\\ 0 &{S}_{vv}\left(\omega \right)& 0 \\{S}_{uu}\left(\omega \right)& 0 &{S}_{ww}\left(\omega \right)\end{matrix}\right\rbrack $
式中: ${S}_{uu}\left(\omega \right),{S}_{vv}\left(\omega \right),{S}_{ww}\left(\omega \right)$ 为 3 个维度上脉动风的自功率谱密度,由式 (10) 确定; ${S}_{uw}\left(\omega \right)$ 为考虑脉动分量 $u$$w$ 的相干,即:
${S}_{uw}\left({i, j,\omega }\right)= \sqrt{{C}_{uw}\left({i,\omega }\right){C}_{uw}\left({j,\omega }\right)}.\\\sqrt{{\operatorname{Coh}}_{u}\left({i, j,\omega }\right){\operatorname{Coh}}_{w}\left({i, j,\omega }\right)} $
式中: ${C}_{uw}$ 为纵向和竖向脉动风速分量互功率谱。
由于风电机组结构一般位于山丘顶部, 山地蜿蜒起伏且存在陡坡现象, 导致各离散点的平均风速和风速谱均不相同,应同时考虑横向( $y$ 方向) 和竖向 $\left({z\text{ 方向 }}\right)$ 相关性。此时采用相干函数表达式为
${\operatorname{Coh}}_{\zeta }\left({i, j,\omega }\right)= \exp \left({-\frac{{2n}\sqrt{{C}_{v\zeta }^{2}{\left({y}_{j}- {y}_{i}\right)}^{2}+ {C}_{z\zeta }^{2}{\left({z}_{j}- {z}_{i}\right)}^{2}}}{\bar{U}\left({z}_{i}\right)+ \bar{U}\left({z}_{j}\right)}}\right)$
式中: ${C}_{y\zeta },{C}_{z\zeta }$ 为无量纲衰减系数,不同方向的功率谱取相对应的值; $\zeta = u, v,{w}_{0}$
以某风电场 $2\mathrm{{MW}}$ 风电机组为算例,风轮直径为 ${100}\mathrm{\;m}$ ,轮毂高度为 ${80}\mathrm{\;m}$ ,风廓线指数取 0.2, 山顶全高 ${1280}\mathrm{\;m}$ ,山体宽度 ${7260}\mathrm{\;m}$ ,山峰迎风一侧 $\tan \alpha ={0.1763}$ ,基于改进的风剖面,考虑塔影效应对应的塔筒直径为 $4\mathrm{\;m}$ ,风场空间网格节点分布采用方形布点,如 图 1 所示。 $z$ 方向是垂直地面方向, $y$ 方向是平行于地面方向。
功率谱密度函数的截断频率取 $5\mathrm{{Hz}}$ ,模拟时程采样间隔 ${\Delta t}= {0.1}\mathrm{\;s}$ ,模拟时间 ${204.8}\mathrm{\;s}$ ,模拟风场高度 ${140}\mathrm{\;m}$ 、宽度 ${120}\mathrm{\;m}$ 。按前文方法编制相应程序,参考风速为轮毂处风速,即 ${10.5}\mathrm{\;m}/\mathrm{s}$ ,对该算例进行模拟, 图 2 ~4 给出了 98 节点、111 节点、 137 节点、163 节点处的三向风速时程图。
风电机组各向风速特征数据如 表 1 所示。从 表 1 中可以看出:随着塔筒高度的增加,湍流强度逐渐减小, 这是由于离地面越高受到地形的影响就越小; 节点 98 处平均风速处于 ${10.5}\mathrm{\;m}/\mathrm{s}$ 以下, 这是因为风力发电机在风场中受到风剪切、塔影效应等因素的影响, 同时直接影响着模拟风速与参考风速的差距大小,并且平均风速在模拟时间内随时间在变化。由此得出风力发电机风场存在一定的非平稳性, 并且纵向平均风速均高于理论值, 这是由于山地的影响导致的。各模拟节点的风场模拟云图如 图 5 所示。
采用工程上常用的功率谱密度函数检验, 即对模拟的非平稳风速通过 FFT 等过程, 获取模拟功率谱, 并通过编制程序计算相应的目标谱, 对比模拟功率谱和目标谱, 验证风场模拟的合理性。 图 6 ~8 分别给出了 98 节点的各个方向考虑风向相关性后的纵向脉动风场的模拟功率谱与目标谱的对比图, 与不考虑风向相关性的纵向脉动风场的模拟功率谱与目标谱的对比图。
图 6 ~8 可知,脉动风的模拟功率谱与目标谱除在非常低的频率点上有差别, 在绝大部分上吻合度都很好。功率谱曲线下的面积表示风速时程的能量,经计算,考虑风向相关性后 3 个方向的目标谱曲线表征能量与模拟谱曲线表征能量之比分别为 1.0384,1.0712,1.1560。说明模拟效果很好; 不考虑风向相关性的目标谱曲线表征能量与模拟谱曲线表征能量之比分别为 1.0567 , 1.1108, 1.1286。通过对比, 考虑风向相关性后, 3 个方向的模拟结果均变得更加准确, 说明方向之间的相关性影响着各个节点及方向的风速模拟。
对各模拟点脉动风速的空间相关性进行验证, 结果如 图 9 所示。
为进一步验证本文方法的有效性, 将实际测量风速和模拟风场误差与其他几种方法进行对比,分别计算风电场实际风速与模拟值的均方误差 MSE、均方根误差 RMSE、平均绝对误差 MAE。 对比结果如 表 2 所示。
表 2 可以看出, 本文所提方法具有更高的辨识精度, 3 种不同误差均远小于其他 3 种方法。
本文根据风电机组风场特点,选用具有自谱和互谱的 Kaimal 模型, 突出纵风向与竖风向的相关性,采用谐波叠加法实现了空间三维非平稳脉动风场的数值模拟。分析结果表明,随着塔筒高度的增加, 湍流强度逐渐减小, 这是由于离地面越高受到地形的影响就越小; 模拟风功率谱与目标功率谱吻合很好, 考虑风向相关性后可提高各节点及方向的吻合度, 提高了风电机组风场模拟的准确性。为能更准确地进行风电机组的结构动力学特性分析和时程响应计算等提供帮助。
  • 国家自然科学基金项目(51678067)
  • 国家自然科学基金青年科学基金项目(51808054)
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  • 接收时间:2022-09-30
  • 首发时间:2025-07-22
  • 出版时间:2024-06-20
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  • 收稿日期:2022-09-30
基金
国家自然科学基金项目(51678067)
国家自然科学基金青年科学基金项目(51808054)
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    1 长沙理工大学 土木工程学院 湖南 长沙 410114
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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