Temporal-spatial cross-correlation analysis of non-stationary near-surface wind speed time series

Temporal-spatial cross-correlation analysis of non-stationary wind speed time series plays a crucial role in wind field reconstruction as well as in wind pattern recognition.Firstly,the near-surface wind speed time series recorded at different locations are studied using the detrended fluctuation analysis(DFA),and the corresponding scaling exponents are larger than 1.This indicates that all these wind speed time series have non-stationary characteristics.Secondly,concerning this special feature( i.e.,non-stationarity)of wind signals,a cross-correlation analysis method,namely detrended cross-correlation analysis(DCCA) coefficient,is employed to evaluate the temporal-spatial cross-correlations between non-stationary time series of different anemometer pairs.Finally,experiments on ten wind speed data synchronously collected by the ten anemometers with equidistant arrangement illustrate that the method of DCCA cross-correlation coefficient can accurately analyze full-scale temporal-spatial cross-correlation between non-stationary time series and also can easily identify the seasonal component,while three traditional cross-correlation techniques(i.e.,Pearson coefficient,cross-correlation function,and DCCA method) cannot give us these information directly.

A preliminary assessment of the sea surface wind speed production of HY-2 scanning microwave radiometer

A scanning microwave radiometer（RM） was launched on August 16,2011,on board HY-2 satellite.The six-month long global sea surface wind speeds observed by the HY-2 scanning microwave radiometer are preliminarily validated using in-situ measurements and WindSat observations,respectively,from January to June 2012.The wind speed root-mean-square（RMS） difference of the comparisons with in-situ data is 1.89 m/s for the measurements of NDBC and 1.72 m/s for the recent four-month data measured by PY30-1 oil platform,respectively.On a global scale,the wind speeds of HY-2 RM are compared with the sea surface wind speeds derived from WindSat,the RMS difference of 1.85 m/s for HY-2 RM collocated observations data set is calculated in the same period as above.With analyzing the global map of a mean difference between HY-2 RM and WindSat,it appears that the bias of the sea surface wind speed is obviously higher in the inshore regions.In the open sea,there is a relatively higher positive bias in the mid-latitude regions due to the overestimation of wind speed observations,while the wind speeds are underestimated in the Southern Ocean by HY-2 RM relative to WindSat observations.

The temporal and spatial variations in the Pacific wind and wave fields for the period 2002–2011

The temporal and spatial variations in the wind and wave fields in the Pacific Ocean between 2002 and 2011 are analyzed using a third-generation wave model（WAVEWATCH III）. The model performance for a significant wave height is validated using in situ buoy data. The results show that the wave model effectively hindcasts the significant wave height in the Pacific Ocean, but the errors are relatively large in the mid- and low-latitude regions. The spatial distributions and temporal variations in a wind speed and the significant wave height in the Pacific Ocean are then considered after dividing the Pacific Ocean into five regions, which show meridional differences and seasonal cycles. Regional mean values are used to give yearly average time series for each separate zone. The high latitude region in the Southern Hemisphere had a stronger significant wave height trend in the model results than regions at other latitudes. The sources and sinks of wave energy are then investigated. Their regional mean values are used to quantify variations in surface waves. Finally, the spectral analyses of the daily mean wind speeds and the significant wave heights are obtained. The significant wave height and the wind speed spectra are found to be connected in some ways but also show certain differences.

Effect of cross-wind on spatial vibration responses of train and track system

By taking cross-wind forces acting on trains into consideration, a dynamic analysis method of the cross-wind and high-speed train and slab track system was proposed on the basis of the analysis theory of spatial vibration of high-speed train and slab track system. The corresponding computer program was written by FORTRAN language. The dynamic responses of the high-speed train and slab track under cross-wind action were calculated. Meanwhile, the effects of the cross-wind on the dynamic responses of the system were also analyzed. The results show that the cross-wind has a significant influence on the lateral and vertical displacement responses of the car body, load reduction factor and overturning factor. For example, the maximum lateral displacement responses of the car body of the first trailer with and without cross-wind forces are 32.10 and 1.60 mm, respectively. The maximum vertical displacement responses of the car body of the first trailer with and without cross-wind forces are 6.60 and 3.29 mm, respectively. The maximum wheel load reduction factors of the first trailer with and without cross-wind forces are 0.43 and 0.22, respectively. The maximum overturning factors of the first trailer with and without cross-wind forces are 0.28 and 0.08, respectively. The cross-wind affects the derailment factor and lateral Sperling factor of the moving train to a certain extent. However, the lateral and vertical displacement responses of rails with the cross-wind are almost the same as those without the cross-wind. The method presented and the corresponding computer program can be used to calculate the interaction between trains and track in cross-wind.

Retrieval of Sea Surface Wind Speed by One-Dimensional Synthetic Aperture Microwave Radiometer

One-dimensional synthetic aperture microwave radiometers have higher spatial resolution and record measurements at multiple incidence angles.In this paper,we propose a multiple linear regression method to retrieve sea surface wind speed at an incidence angle between 0°65°.We assume that a one-dimensional synthetic aperture microwave radiometer operates at frequencies of 6.9,10.65,18.7,23.8 and 36.5 GHz.Then,the microwave radiative transfer forward model is used to simulate the measured brightness temperatures.The sensitivity of the brightness temperatures at 0°65°to the sea surface wind speed is calculated.Then,vertical polarization channels(VR),horizontal polarization channels(HR)and all channels(AR)are used to retrieve the sea surface wind speed via a multiple linear regression algorithm at 0°65°,and the relationship between the retrieval error and incidence angle is obtained.The results are as follows:(1)The sensitivity of the vertical polarization brightness temperature to the sea surface wind speed is smaller than that of the horizontal polarization.(2)The retrieval error increases with Gaussian noise.The retrieval error of VR first increases and then decreases with increasing incidence angle,the retrieval error of HR gradually decreases with increasing incidence angle,and the retrieval error of AR first decreases and then increases with increasing incidence angle.(3)The retrieval error of AR is the lowest and it is necessary to retrieve the sea surface wind speed at a larger incidence angle for AR.

风电机组数据采集与监控系统异常数据识别方法

为了解决原始的风电机组数据采集与监控系统(SCADA)中包含大量异常记录的数据、难以准确反映机组运行状态的问题,提出了一种带噪声基于密度的空间聚类(DBSCAN)模型的风电机组SCADA异常数据识别方法。该方法从分析风速-功率曲线的特点出发,采用预测误差和分类准确度来选取关键聚类参数邻域半径和邻域最小样本点数,避免了人工确定聚类参数的主观性,且参数选择过程可以完全自动化,实现了风电机组SCADA异常数据的有效识别。通过某风场中风电机组的监测数据进行实例验证,结果表明:所提方法能够在保证异常数据被剔除的前提下,保留尽可能多的正常数据,异常识别效果好于现有的k-dist图法和基于k-平均最近邻算法的改进算法(KANN-DBSCAN)。该研究可为开展风电机组状态分析提供参考。

基于改进Pair-Copula方法的多风电场风速时空相关性建模

传统Pair-Copula方法采用藤结构表示风电场风速之间的相依结构,能充分计及风电场风速之间的空间相关性,但缺乏风速时间尺度上相关性的考虑。针对此问题,文章提出了一种基于改进Pair-Copula的风速建模方法。该方法首先将藤结构中风电场风速的Copula联合分布函数样本点所包含的自相关信息转移至抽样模拟的随机数中,以此体现风电场风速数据在时间序列上的波动规律性。然后,采用分布更均匀的Sobol序列随机数代替普通随机数进行抽样,以提高风速概率特性模拟的准确性。最后,以位于美国东部的多个相邻风电场实测风速数据为例进行算例分析,算例结果验证了所提风电场风速建模方法的有效性。

基于时空注意力-Seq2Seq模型的多风电机组多步风速预测算法

针对在多风电机组风速预测任务中,卷积运算不适用于提取排布不规则的多风电机组空间相关性的问题,提出一种基于时空注意力-Seq2Seq模型的多风电机组多步风速预测算法。首先使用空间注意力机制强化风速序列的空间相关性,并对常规空间注意力机制进行改进;之后使用Seq2Seq模型中的编码器进行编码;最后使用结合时间注意力机制的解码器计算多风电机组的多步预测结果。以河北市某风电场的实际数据为算例进行实验,结果表明相比其他对比算法,所提算法的平均绝对误差下降约4.3%~15.0%,精度有较大提高。

基于弯剪梁模型和Von Karman风速谱的高层建筑风振系数实用算法

为使风振系数计算中采用的振型函数更符合高层建筑振型特点且易于计算,采用基于弯剪梁模型的高层建筑基本振型简化算式,同时采用Von Karman与高度有关的风速谱模型和Davenport与频率有关的空间相关性模型,建立高层建筑风振系数计算的实用算式,并通过算例与我国现行荷载规范中的风振系数算式进行比较。结果表明,该方法考虑了不同高层建筑振型的特点,既提高了计算精度,简单实用,又便于与国际主流荷载规范接轨。

1960-2017年黄土高原地区风速时空变化特征

[目的]揭示黄土高原地区风速时空变化特征,为区域防灾减灾提供参考。[方法]基于黄土高原59个气象站1960—2017年逐月风速实测数据,采用线性回归、Mann-Kendall检验和滑动t检验等方法,对黄土高原地区历年及四季风速的时空变化趋势进行了研究。[结果](1)1960—2017年黄土高原年均风速以-0.0075 m/(s·a)速率显著下降,春季降幅最大,其次为冬、秋季,夏季降幅最小;年内月平均风速先升后降,4月风速下降最快,7月下降最慢。(2)黄土高原历年及夏、秋、冬季风速均在1980s突变减少,春季平均风速未发生突变,冬季风速突变后较突变前降幅最大,秋季最小。(3)黄土高原历年均风速、历年及四季风速变率均表现出西北高、东南低的特征,站点呈下降趋势的春季最多,夏季最少。(4)AO指数的上升、气候变暖及地表植被覆盖增加等,可能不同程度地造成黄土高原风速长期下降趋势。[结论]黄土高原区应将春季作为防风御风重点时段,将西北部地区作为重点防御地区,增强防灾减灾能力建设。

Spatial dispersion of wind speeds and its influence on the forecasting error of wind power in a wind farm

Big wind farms must be integrated to power system.Wind power from big wind farms,with randomness,volatility and intermittent,will bring adverse impacts on the connected power system.High precision wind power forecasting is helpful to reduce above adverse impacts.There are two kinds of wind power forecasting.One is to forecast wind power only based on its time series data.The other is to forecast wind power based on wind speeds from weather forecast.For a big wind farm,due to its spatial scale and dynamics of wind,wind speeds at different wind turbines are obviously different,that is called wind speed spatial dispersion.Spatial dispersion of wind speeds and its influence on the wind power forecasting errors have been studied in this paper.An error evaluation framework has been established to account for the errors caused by wind speed spatial dispersion.A case study of several wind farms has demonstrated that even ifthe forecasting average wind speed is accurate,the error caused by wind speed spatial dispersion cannot be ignored for the wind power forecasting of a wind farm.

Spatial estimation of wind speed:a new integrative model using inverse distance weighting and power law

Spatial interpolation(SI)is currently one of the most common ways to estimate wind speed(Ws).However,classic SI models either ignore the complex geography[e.g.inverse distance weighting(IDW)],or demand high computational resources(e.g.cokriging).This study aimed to develop a simple yet effective SI model for estimating Ws in Eastern Thrace of Turkey.This new method,named MIDW(Ws),is a modified IDW through the integration of IDW with wind profile model,power law(PL),representing the influence of land cover and topography on Ws.Terrain features and elevation data of PL were obtained using normalized difference vegetation index(NDVI)and digital elevation model(DEM),respectively.Results showed superior and comparable performance of MIDW(Ws)to standard IDW and ordinary kriging(OK)across all months of year.Compared to ordinary cokriging(OCK)using DEM as covariate,MIDW(Ws)generated better results in the arid–semiarid seasons(around summer).Local complex atmospheric conditions during rainy seasons(around winter)may have affected the performance of incorporating PL with MIDW(Ws).Generally,the proposed MIDW(Ws)is simpler and easier to implement compared to OCK.For landscape-scale projects,its high computational efficiency and relatively robust performance show potential to deal with large volumes of datasets.

基于MOS的精细化风速图谱快速生成方法

针对风电场前期规划阶段对风能资源分布情况的需求,提出了一种适用于平坦地形风速图谱快速生成的方法。以山东省风电重点开发区域潍坊市、滨州市和东营市为研究区域,利用Global Wind Atlas(GWA)提供的矢量化年平均风速模拟数据和收集到的测风塔实测风速,建立线性模式输出统计(简称MOS)模型。采用最小二乘法、反距离权重(IDW)插值和普通克里金(Kriging)插值3种方法对研究区域100 m高度GWA风速图谱进行订正。结果表明:①3种订正方法中最小二乘法和普通Kriging插值订正效果较优;②与原GWA风速图谱相比,订正后的风速更接近实测风速,订正方法有效提高了风速图谱的整体精度。该方法实现便捷,可以较好的体现区域内风速的空间分布情况,适用于平坦地形风能资源的研究与评估,可为工程规划、设计提供参考。

福州市地面逐时最大风时空分布特征分析

利用2010—2021年福州市地面最大风资料,结合统计和图示法,分析逐时最大风速及对应风向时空分布特征。结果表明:福州市地面逐时最大风速年均值呈减小趋势,静风次数明显增多。月均值呈双峰型,5月最小,10月最大;日变化为单峰型。分风向均值最大为NNE,最小为W。年概率最大风向是NE,最小是W。6—7月主导风向为S,其余月份主导风向为NE。空间分布地域性和季节性明显,高海拔区和沿海南部是风速均值大值分布区,有明显年份突增,且多在中午到傍晚出现。西南地区和罗源湾附近静风占比较高,闽江流域是SW-NW高发区。1月和10—12月风向较一致,2—6月和9月风向较散乱。

中国参考作物腾发量时空变化特性分析

分析参考作物腾发量的时空变化特征，有助于了解中国农业及生态需水的分布与演变规律。基于全国范围200多个气象站测站逐日气象观测资料，应用FAO-Penman-Monteith公式，计算得出各站历年逐日参照作物腾发量ET0。利用GIS的空间分析功能，采用反距离空间插值方法得到全国参考腾发量的分布图，统计分析了不同分区不同时段ET0的变化情况。结果表明：西北河西走廊地区和南方岭南地区的参考作物腾发量较大，最大值超过1500mm。而东北黑龙江一带和四川盆地附近，参考作物腾发量较小，在600～700mm之间。此外，夏季ET0的分布特征决定了全年ET0的分布特征。选取4个代表气象站，对其ET0的历年变化及其与气象因素的关系进行了分析。分析表明，受风速减小和气温增加的共同影响，干旱地区、半干旱地区和半湿润地区的参考作物腾发量呈现减少趋势，湿润地区则相对稳定。

考虑风速时空分布及风机运行状态的风电场功率计算方法

大规模风电并网背景下,准确计算风电场输出功率对调度控制和电网安全运行至关重要。以区域内测风塔观测的分钟到小时级平均风速和风向为输入,研究风机地理分布以及风机启停对尾流模型的影响,计及风速在空间分布的时延效应,建立了考虑风速时空分布特性的等效风速模型。在此基础上,进一步考虑风机的机械特性及运行状态,提出了风电场功率计算方法。以某实际风电场地理数据和风机参数为基础的仿真算例验证了所提方法的可行性和合理性。

西双版纳热带季节雨林风时空变化特征初步分析

利用西双版纳热带季节雨林观测铁塔不同高度的风速及风向观测资料．分析了风的年、季节和日变化特征．结果表明，林冠上风速较强，林冠下风速较弱；林内风速的日变化和垂直变化均不明显．30～50m范围内，风速垂直变化最显著，但年变化不大；50m以上风速年变化显著，但垂直变化稍小．于热季（3～4月）风速最大，雨季（5～10月）次之，雾凉季（11-翌年2月）最小，昼间风速大于夜间．在昼问，上午风速最小，下午次之，中午最大．受地理位置和地形影响，风向具有明显的日变化特征，主导风向昼间为偏东南风，夜间为偏西风．昼间零平面位移（d）值上午最大，中午次之下午最小，其年变化幅度呈现下午幅度大，上午和中午幅度小的趋势．粗糙度（Z0）昼间值呈现下午〉中午〉上午的趋势，且下午Z0值显著大于其他两个时段。

关于利用空间相关性预测风速的评述

归纳了空间相关性风速预测的现状;引入条件相关性及相应的可信相关度概念,以代替常规的相关性;基于大数据思维,提出将数据驱动与因果驱动相结合的预测框架。从历史数据中挖掘相关性,利用空间相关性增加风速预测的数据源,部分克服历史数据缺失的困难;利用大时滞的空间相关性,有助于预测下游风速的突变。最后,依托该框架展望了空间相关性风速预测的前景。

基于空间相关性的风电功率预测研究综述

由于风电具有很强的随机性和波动性,因此大规模风电并网会对电力系统的运行和稳定性造成很大的影响。如何准确预测区域风电场的功率已经成为当今电力系统亟待解决的研究课题。现有的风电功率预测方法未考虑空间相关因素,预测体系有待进一步完善。基于空间相关性的风电功率预测是一种考虑了本地信息和空间相关信息的综合预测方法。文中给出了基于空间相关性的风电功率预测的定义、概念和基本特点,分别从统计模型、物理模型、空间降尺度过程和空间升尺度过程4个方面详细阐述了基于空间相关性的风电功率预测的实现方法,并对空间相关性在风电功率预测方面应用的最新国内外研究进展作了系统的分析评述。最后,针对该领域尚存在的问题与不足,总结了今后的发展方向和需要进一步探索的研究内容。

基于季风提高空间相关性预测的优化延迟时间

由于只依据本地数值天气预报和历史数据的风电功率预测大体进入相对饱和时期,所以依据周边地区风速和风向的空间相关性预测就成为今后提高风速预测效果的主要方法。我国三分之二的国土面积,陆地风能资源丰富区中的沿海和北部的部分区域,以及主要能源消费区都处在季风区,因此利用季风改进空间相关性预测就成为重要的研究课题。季风区不同地点风速时间序列之间,存在显著的延迟相似性。利用相关系数、互信息等可以提取这些优化的延迟时间,并依此实现风速的空间相关性预测。在季风时期,福建长江澳对广东湛江的优化延迟时间约为20 h,明显超过欧美典型的4 h。