Performance of the CMA-GD Model in Predicting Wind Speed at Wind Farms in Hubei, China

This study assesses the predictive capabilities of the CMA-GD model for wind speed prediction in two wind farms located in Hubei Province,China.The observed wind speeds at the height of 70m in wind turbines of two wind farms in Suizhou serve as the actual observation data for comparison and testing.At the same time,the wind speed predicted by the EC model is also included for comparative analysis.The results indicate that the CMA-GD model performs better than the EC model in Wind Farm A.The CMA-GD model exhibits a monthly average correlation coefficient of 0.56,root mean square error of 2.72 m s^(-1),and average absolute error of 2.11 m s^(-1).In contrast,the EC model shows a monthly average correlation coefficient of 0.51,root mean square error of 2.83 m s^(-1),and average absolute error of 2.21 m s^(-1).Conversely,in Wind Farm B,the EC model outperforms the CMA-GD model.The CMA-GD model achieves a monthly average correlation coefficient of 0.55,root mean square error of 2.61 m s^(-1),and average absolute error of 2.13 m s^(-1).By contrast,the EC model displays a monthly average correlation coefficient of 0.63,root mean square error of 2.04 m s^(-1),and average absolute error of 1.67 m s^(-1).

Wind Speed and Power Density Analysis for Sustainable Energy in Batouri, East Region of Cameroon

This paper develops the modeling of wind speed by Weibull distribution in the intention to evaluate wind energy potential and help for designing small wind energy plant in Batouri in Cameroon. The Weibull distribution model was developed using wind speed data collected from a metrological station at the small Airport of Batouri. Four numerical methods (Moment method, Graphical method, Empirical method and Energy pattern factor method) were used to estimate weibull parameters K and C. The application of these four methods is effective using a sample wind speed data set. With some statistical analysis, a comparison of the accuracy of each method is also performed. The study helps to determine that Energy pattern factor method is the most effective (K = 3.8262 and C = 2.4659).

Short-term wind speed forecasting bias correction in the Hangzhou area of China based on a machine learning model

准确的风速预报具有重要的社会意义.在本研究中,使用名为WSFBC-XGB的XGBoost机器学习模型对中国浙江省杭州市自动气象站的短期风速预报误差进行校正.WSFBC-XGB使用本地数值天气预报系统的产品作为输入.将WSFBC-XGB校正的结果与传统MOS(模型输出统计)方法校正的结果进行了比较.结果表明:WSFBC-XGB预报风速的均方根误差(RMSE)/准确率(ACC)分别比NWP和MOS降低/提高了26.1%和7.64%/35.6%和7.02%;对于90%的站点WSFBC-XGB的RMSE/ACC均小于/高于MOS.此外,采用平均杂质减少法对WSFBC-XGB的可解释性进行分析,以帮助用户增加对模型的信任.结果表明:10米风速(47.35%),10米风的经向分量(12.73%),日循环(9.97%)和1000百帕风的经向分量(7.45%)是前4个最重要的特征.WSFBC-XGB模型将有助于提高短期风速预报的准确性,为大型户外活动提供支持.

Changes in Wind Speed and Extremes in Beijing during 1960–2008 Based on Homogenized Observations

Daily observations of wind speed at 12 stations in the Greater Beijing Area during 1960–2008 were homogenized using the Multiple Analysis of Series for Homogenization method. The linear trends in the regional mean annual and seasonal （winter, spring, summer and autumn） wind speed series were-0.26,-0.39,-0.30,-0.12 and-0.22 m s-1 （10 yr）-1 , respectively. Winter showed the greatest magnitude in declining wind speed, followed by spring, autumn and summer. The annual and seasonal frequencies of wind speed extremes （days） also decreased, more prominently for winter than for the other seasons. The declining trends in wind speed and extremes were formed mainly by some rapid declines during the 1970s and 1980s. The maximum declining trend in wind speed occurred at Chaoyang （CY）, a station within the central business district （CBD） of Beijing with the highest level of urbanization. The declining trends were in general smaller in magnitude away from the city center, except for the winter case in which the maximum declining trend shifted northeastward to rural Miyun （MY）. The influence of urbanization on the annual wind speed was estimated to be about-0.05 m s-1 （10 yr）-1 during 1960–2008, accounting for around one fifth of the regional mean declining trend. The annual and seasonal geostrophic wind speeds around Beijing, based on daily mean sea level pressure （MSLP） from the ERA-40 reanalysis dataset, also exhibited decreasing trends, coincident with the results from site observations. A comparative analysis of the MSLP fields between 1966–1975 and 1992–2001 suggested that the influences of both the winter and summer monsoons on Beijing were weaker in the more recent of the two decades. It is suggested that the bulk of wind in Beijing is influenced considerably by urbanization, while changes in strong winds or wind speed extremes are prone to large-scale climate change in the region.

Study on the operational safety of high-speed trains exposed to stochastic winds

The characteristic wind curve （CWC） was com- monly used in the previous work to evaluate the operational safety of the high-speed trains exposed to crosswinds. How- ever, the CWC only provide the dividing line between safety state and failure state of high-speed trains, which can not evaluate the risk of derailment of high-speed trains when ex- posed to natural winds. In the present paper, a more realistic approach taking into account the stochastic characteristics of natural winds is proposed, which can give a reasonable and effective assessment of the operational safety of high-speed trains under stochastic winds. In this approach, the longitudi- nal and lateral components of stochastic winds are simulated based on the Cooper theory and harmonic superposition. An algorithm is set up for calculating the unsteady aerody- namic forces （moments） of the high-speed trains exposed to stochastic winds. A multi-body dynamic model of the rail vehicle is established to compute the vehicle system dynamic response subjected to the unsteady aerodynamic forces （mo- ments） input. Then the statistical method is used to get the mean characteristic wind curve （MCWC） and spread range of the high-speed trains exposed to stochastic winds. It is found that the CWC provided by the previous analyticalmethod produces over-conservative limits. The methodol- ogy proposed in the present paper can provide more signif- icant reference for the safety operation of high-speed trains exposed to stochastic winds.

Wavelet-Based Nonstationary Wind Speed Model in Dongting Lake Cable-Stayed Bridge

The wind-rain induced vibration phenomena in the Dongting Lake Bridge (DLB) can be observed every year, and the field measurements of wind speed data of the bridge are usually nonstationary. Nonstationary wind speed can be decomposed into a deterministic time-varying mean wind speed and a zero-mean stationary fluctuating wind speed component. By using wavelet transform (WT), the time-varying mean wind speed is extracted and a nonstationary wind speed model is proposed in this paper. The wind characteristics of turbulence intensity, integral scale and probability distribution of the bridge are calculated from the typical wind samples recorded by the two anemometers installed on the DLB using the proposed nonstationary wind speed model based on WT. The calculated results are compared with those calculated by the empirical mode decomposition (EMD) and traditional approaches. The compared results indicate that the wavelet-based nonstationary wind speed model is more reasonable and appropriate than the EMD-based nonstationary and traditional stationary models for characterizing wind speed in analysis of wind-rain-induced vibration of cables.

A Statistical Analysis of Wind Speed and Power Density Based on Weibull and Rayleigh Models of Jumla, Nepal

In the present study, wind speed data of Jumla, Nepal have been statistically analyzed. For this purpose, the daily averaged wind speed data for 10 year period (2004-2014: 2012 excluded) provided by Department of Hydrology and Meteorology (DHM) was analyzed to estimate wind power density. Wind speed as high as 18 m/s was recorded at height of 10 m. Annual mean wind speed was ascertained to be decreasing from 7.35 m/s in 2004 to 5.13 m/s in 2014 as a consequence of Global Climate Change. This is a subject of concern looking at government’s plan to harness wind energy. Monthly wind speed plot shows that the fastest wind speed is generally in month of June (Monsoon Season) and slowest in December/January (Winter Season). Results presented Weibull distribution to fit measured probability distribution better than the Rayleigh distribution for whole years in High altitude region of Nepal. Average value of wind power density based on mean and root mean cube seed approaches were 131.31 W/m2/year and 184.93 W/m2/year respectively indicating that Jumla stands in class III. Weibull distribution shows a good approximation for estimation of power density with maximum error of 3.68% when root mean cube speed is taken as reference.

A Physics-Based Dual-Frequency Approach for Altimeter Wind Speed Retrieval

The altimeter normalized radar cross section(NRCS) has been used to retrieve the sea surface wind speed for decades, and more than a dozen of wind speed retrieval algorithms have been proposed. Despite the continuing efforts to improve the wind speed measurements, a bias dependence on wave state persists in all wind algorithms. On the basis of recent evidence that short waves are essentially modulated by local winds and much less affected by wave state, we proposed a physics-based approach to retrieve the wind speed from the dual-frequency difference in terms of the mean square slope of short waves. A collocated dataset of coincident altimeter/buoy measurements were used to develop and validate the approach. Validation against buoy measurements indicates that the approach is almost unbiased and has an overall root mean square error of 1.24 m s-1, which is 5.3% lower than the single-parameter algorithm in operational use(Witter and Chelton, 1991) and 2.4% lower than another dual-frequency approach(Chen et al., 2002). Furthermore, the results indicate that the new approach significantly improves the wave-dependent bias compared to the single-parameter algorithm. The capacity of altimeter to retrieve sea surface wind speed appears to be limited for the case of winds below 3 m s-1. The validity of the approach at high winds needs to be further examined in the future study.

Applicability evaluation of ERA5 wind and wave reanalysis data in the South China Sea

Wind and wave data are essential in climatological and engineering design applications.In this study,data from 15 buoys located throughout the South China Sea(SCS)were used to evaluate the ERA5 wind and wave data.Applicability assessment are beneficial for gaining insight into the reliability of the ERA5 data in the SCS.The bias range between the ERA5 and observed wind-speed data was-0.78-0.99 m/s.The result indicates that,while the ERA5 wind-speed data underestimation was dominate,the overestimation of such data existed as well.Additionally,the ERA5 data underestimated annual maximum wind-speed by up to 38%,with a correlation coefficient>0.87.The bias between the ERA5 and observed significant wave height(SWH)data varied from-0.24 to 0.28 m.And the ERA5 data showed positive SWH bias,which implied a general underestimation at all locations,except those in the Beibu Gulf and centralwestern SCS,where overestimation was observed.Under extreme conditions,annual maximum SWH in the ERA5 data was underestimated by up to 30%.The correlation coefficients between the ERA5 and observed SWH data at all locations were greater than 0.92,except in the central-western SCS(0.84).The bias between the ERA5 and observed mean wave period(MWP)data varied from-0.74 to 0.57 s.The ERA5 data showed negative MWP biases implying a general overestimation at all locations,except for B1(the Beibu Gulf)and B7(the northeastern SCS),where underestimation was observed.The correlation coefficient between the ERA5 and observed MWP data in the Beibu Gulf was the smallest(0.56),and those of other locations fluctuated within a narrow range from 0.82 to 0.90.The intercomparison indicates that during the analyzed time-span,the ERA5 data generally underestimated wind-speed and SWH,but overestimated MWP.Under non-extreme conditions,the ERA5 wind-speed and SWH data can be used with confidence in most regions of the SCS,except in the central-western SCS.

不同建筑环境下平均风速剖面的数值模拟

基于计算流体力学(CFD)方法对具有不同建筑环境参数的城市上空的风场进行系统性的数值模拟,并对平均风速剖面指数的变化规律进行讨论。研究结果表明,建筑平均高度对平均风速剖面指数的影响较大,建筑密度对平均风速剖面指数的影响较小。文中还给出了以建筑平均高度和建筑密度为自变量的平均风速剖面指数经验公式。

对数律和指数律风剖面的差异分析及其对桥梁抗风性能评价的影响

现有的公路桥梁抗风设计采用指数律风速剖面来计算各个基准高度的平均风速,然而气象学实践和一些国家规范中早已采用对数律风速剖面,两种风速剖面在不同高度处的平均风速差异及其对桥梁抗风的影响应当值得关注。对比了不同地表粗糙类型下对数律模型和指数律模型在不同高度处的风速差异,并采用ANSYS有限元软件计算了实桥在风荷载下的位移和桥塔弯矩差异。结果表明,在地表粗糙度较大和离地高度较大时,对数律和指数律模型的风速差异较为明显,风荷载的差异对桥梁内力和位移造成的影响不可忽略。有必要进一步研究这两种风速剖面模型在高空处的准确性,以更好地指导桥梁的抗风设计。

基于SVM的我国北部沿岸海上风速估算方法研究

沿岸海上观测站点稀少,而沿岸陆地观测站点相对密集,开展海陆分布导致的海陆风速差异特征研究,实现由陆地观测风速估算海上风速,有助于提高海上大风预报服务能力。利用我国北方地区两组浮标及其邻近陆地观测站点的2016—2020年逐小时平均风速和阵风风速数据,统计分析海陆风速差异特征及规律,采用支持向量机方法,构建了基于陆地平均风速、陆地阵风风速、海陆站点距离、月份及观测时次的海上风速估算模型。利用另外两组海陆观测站2021年观测数据对估算模型进行检验,结果表明:对于6级及以上的平均风速和7级及以上的阵风风速,模型具有较高的估算准确率,模型估算的两个检验组的海上站点平均风速(阵风风速)RMSE分别为2.40 m·s^(-1)(3.20 m·s^(-1))和2.35 m·s^(-1)(2.57 m·s^(-1)),较ERA5分别减少了24%(14%)和23%(20%)。在一次温带气旋和冷空气共同影响的大风过程中,模型估算的两个检验组的海上平均风速(阵风风速)平均绝对误差分别为1.6 m·s^(-1)(2.3 m·s^(-1))和1.1 m·s^(-1)(1.5 m·s^(-1)),在极值时刻的平均风速(阵风风速)误差分别为-1.3 m·s^(-1)(-0.6 m·s^(-1))和-1.2 m·s^(-1)(-3.1 m·s^(-1)),均优于ERA5计算结果。基于支持向量机的海上风速估算模型能够利用陆地观测风速估算出较为准确的海上大风,可降低海上观测资料不足的影响,具有一定的应用前景。

基于k-means聚类的SVR短期风速预测

准确的风速预测是风力发电功率预测的重要基础。为了进一步提高风速预测精度,文章提出一种基于k-means聚类的支持向量回归机(SVR)的短期风速组合预测新方法。首先分析影响风速变化的因素,计算不同风速属性相对于风速序列的皮尔逊相关系数(PCC)值,并对其进行加权;然后采用k-means聚类方法对风速样本进行聚类;再利用SVR针对每组样本建模;最后结合实际风电场进行仿真,结果表明,该方法具有较高的准确性和可行性。

Investigations into Some Simple Expressions of the Gamma Function in Wind Power Theoretical Estimate by the Weibull Distribution

The Weibull distribution is a probability density function (PDF) which is widely used in the study of meteorological data. The statistical analysis of the wind speed v by using the Weibull distribution leads to the estimate of the mean wind speed , the variance of v around and the mean power density in the wind. The gamma function Γ is involved in those calculations, particularly Γ (1+1/k), Γ (1+2/k) and Γ (1+3/k). The paper reports the use of the Weibull PDF f(v) to estimate the gamma function. The study was performed by looking for the wind speeds related to the maximum values of f(v), v2 f(v) and v3 f(v). As a result, some approximate relationships were obtained for Γ (1+1/k), Γ (1+2/k) and Γ (1+3/k), that use some fitting polynomial functions. Very good agreements were found between the exact and the estimated values of Γ (1+n/k) that can be used for the estimation of the mean wind speed , the variance σ2 of the wind speed v;around the mean speed and the average wind power density.

西藏高原风场实测及非平稳风特性研究

为研究高原风特性,于海拔4465 m的西藏南部浪卡子县离地10 m高度处进行了1年4季昼夜的风速风向实时连续采样实测,筛选出1256个有效大风样本.采用平稳模型、EMD非平稳模型及最优时变均值非平稳模型处理实测非平稳风速样本,分析了3种风速模型的平均风速、湍流强度、阵风因子、湍流积分尺度和脉动风谱等风场特性参数,并与低海拔地区多种类别风场实测结果进行对比.结果表明:西藏高原冬半年脉动风湍流强度大于夏半年;在相似地貌下,高原脉动风湍流强度要高于低海拔地区,而与一些特殊地貌及特殊风场如峡湾风、台风等的湍流强度接近;湍流积分尺度计算结果受风速平稳程度影响很大,平稳模型高估了湍流积分尺度;西藏高原脉动风拟合风谱与各标准谱均有一定差别,且具有较大的高频能量.因此,高原建筑结构抗风设计应注意高原脉动风参数与低海拔地区的区别,并充分考虑高原风的非平稳性,合理选择非平稳模型.

基于聚类机群关联拓扑的时空图记忆风速超短期预测

为提高规模化风电场风速的预测精度及计算效率,提出一种聚类机群最优关联拓扑,并构建时空图记忆模型预测风速。分析机群平均风速波动特性,构建平抑度指标;考虑风机间风向关联特性,定义风向相似度因子,并嵌入k-means聚类,提高类内风速互补性。结合互信息量化分析各子机群风机的相关性,构建最优有向关联拓扑。结合关联拓扑及风速关联属性建立机群风速时空图数据集合,输入时空图记忆网络,利用图注意力提取空间特征,并结合记忆网络处理时序信息,输出机群平均风速超短期预测结果。最后将模型应用于实际风电场的风速预测,通过对比分析,验证了所提方法的准确性和有效性。

平均风效应对悬索桥全过程颤振性能的影响研究

为准确评估大跨悬索桥的颤振性能,建立平均风与气弹效应一体化气动力时域模型,研究平均风效应对悬索桥颤振临界风速与后颤振响应特性的影响。采用阶跃函数法建立悬索桥加劲梁断面的气动自激力时域模型,并通过伪稳态气动力扣除法解决时域分析中直接叠加气动自激力与平均风荷载会导致部分气动力被重复考虑的问题,从而构建平均风与气弹效应一体化气动力时域模型。以某大跨悬索桥为背景,基于该模型分析该桥加劲梁的颤振临界风速和后颤振性能。结果表明:平均风效应对加劲梁颤振临界风速具有一定的影响,考虑平均风效应后,加劲梁颤振临界风速稍有提高;平均风效应对加劲梁后颤振响应特性影响显著,考虑平均风效应后,加劲梁后颤振响应达到稳态极限环振动(LCO)所需的时间比不考虑平均风效应时明显缩短,且最终的LCO幅值也有所增大;后颤振频率随着风速的增加呈现递减趋势,相同风速下,考虑平均风效应时的振动频率比不考虑时有所降低。

北斗测风与L波段雷达测风的差异性分析

利用湖北省宜昌国家基本气象站获得的北斗(以下简称BD)测风和L波段雷达(以下简称L)测风资料,对获取的高频率采样秒数据,通过不同的滑动方式处理后,比较分析了BD与L的风向以及东风、北风风速的一致性及差异情况。结果表明,BD与L的东风、北风风速以及两者探测的风向差异均较小,具有较好的一致性,相关系数均能达到0.99;在具备足够多的试验资料时,采样数据的滑动处理对数据整体结果分析的影响较小,但对具体差异仍有一定影响,总体上,对测风差异结果的影响为:风向>东风风速>北风风速;在对较高采样频率的数据使用时,特别是在试验资料较少时,需正确选用合理的处理方式,以消除由于外界因素造成的虚假信息。

峡谷地形平均风速特性与加速效应

采用计算流体力学(CFD)方法建立多个数值模型,通过与风洞试验的对比分析验证了数值模拟结果的可靠性,较系统地研究并详细分析了峡谷长度、山顶间距、山脉坡度3种地貌因素对平均风加速效应的影响.结果表明:山脉顶部加速效应主要受山脉坡度的影响,在近地面内坡度越大加速效应越明显;峡谷内部加速效应受多种地貌因素影响且变化趋势较为复杂,必须考虑峡谷侧坡边界层的影响和流动的三维效应,当峡谷长度越短、山顶间距越小、山脉坡度越大时,迎风谷口处在近地面内的加速效应越明显.最后计算出典型峡谷的风压地形修正系数,并与我国建筑结构荷载规范进行对比.