Joint Return Value Estimation of Significant Wave Heights and Wind Speeds with Bivariate Copulas

The joint design criteria of significant wave heights and wind speeds are quite important for the structural reliability of fixed offshore platforms.However,the design method that regards different ocean environmental variables as independent is conservative.In the present study,we introduce a bivariate sample consisting of the maximum wave heights and concomitant wind speeds of the threshold by using the peak-over-threshold and declustering methods.After selecting the appropriate bivariate copulas and univariate distributions and blocking the sample into years,the bivariate compound distribution of annual extreme wave heights and concomitant wind speeds is constructed.Two joint design criteria,namely,the joint probability density method and the conditional probability method,are applied to obtain the joint return values of significant wave heights and wind speeds.Results show that(28.5±0.5)m s^(-1)is the frequently obtained wind speed based on the Atlantic dataset,and these joint design values are more appropriate than those calculated by univariate analysis in the fatigue design.

Joint probability distribution of winds and waves from wave simulation of 20 years (1989-2008) in Bohai Bay

The joint probability distribution of wind speed and significant wave height in the Bohai Bay was investigated by comparing the Gurnbel logistic model, the Gumbel-Hougaard （GH） copula function, and the Clayton copula function. Twenty years of wind data from 1989 to 2008 were collected from the European Centre for Medium-Range Weather Forecasts （ECMWF） database and the blended wind data of the Quick Scatterometer （QSCAT） satellite data set and re-analysis data from the United States National Centers for Environmental Prediction （NCEP）. Several typhoons were taken into account and merged with the background wind fields from the ECMWF or QSCAT/NCEP database. The 20-year data of significant wave height were calculated with the unstructured-grid version of the third-generation wind wave model Simulating WAves Nearshore （SWAN） under extreme wind process conditions. The Gumbel distribution was used for univariate and marginal distributions. The distribution parameters were estimated with the method of L-moments. Based on the marginal distributions, the joint probability distributions, the associated return periods, and the conditional probability distributions were obtained. The GH copula function was found to be optimal according to the ordinary least squares （OLS） test. The results show that wind waves are the prevailing type of wave in the Bohai Bay.

Joint Model of Wind Speed and Corresponding Direction Based on Wind Rose for Wind Energy Exploitation

As a common and extensive datum to analyze wind,wind rose is one of the most important components of the meteorological elements.In this study,a model is proposed to establish the joint probability distribution of wind speed and direction using grouped data of wind rose.On the basis of the model,an algorithm is presented to generate pseudorandom numbers of wind speed and paired direction data.Afterward,the proposed model and algorithm are applied to two weather stations located in the Liaodong Gulf.With the models built for the two cases,a novel graph representing the continuous joint probability distribution of wind speed and direction is plotted,showing a strong correlation to the corresponding wind rose.Moreover,the joint probability distributions are utilized to evaluate wind energy potential successfully.In cooperation with Monte Carlo simulation,the model can approximately predict annual directional extreme wind speed under different return periods under the condition that the wind rose can represent the meteorological characters of the wind field well.The model is beneficial to design and install wind turbines.

Multi-hazard joint probability distribution model for wind speed,wind direction and rain intensity

Multiple disasters such as strong wind and torrential rain pose great threats to civil infrastructures.However,most existing studies ignored the dependence structure between them,as well as the effect of wind direction.From the dimension of the engineering sector,this paper introduces the vine copula to model the joint probability distribution(JPD)of wind speed,wind direction and rain intensity based on the field data in Yangjiang,China during 1971–2020.First,the profiles of wind and rain in the studied area are statistically analyzed,and the original rainfall amounts are converted into short-term rain intensity.Then,the marginal distributions of individual variables and their pairwise dependence structures are built,followed by the development of the trivariate joint distribution model.The results show that the constructed vine copula-based model can well characterize the dependence structure between wind speed,wind direction and rain intensity.Meanwhile,the JPD characteristics of wind speed and rain intensity show significant variations depending on wind direction,thus the effect of wind direction cannot be neglected.The proposed JPD model will be conducive for reasonable and precise performance assessment of structures subjected to multiple hazards of wind and rain actions.

A Study of Wind Statistics Through Auto-Regressive and Moving-Average (ARMA) Modeling

Statistical properties of winds near the Taichung Harbour are investigated. The 26 years'incomplete data of wind speeds, measured on an hourly basis, are used as reference. The possibility of imputation using simulated results of the Auto-Regressive (AR), Moving-Average (MA), and/ or Auto-Regressive and Moving-Average (ARMA) models is studied. Predictions of the 25-year extreme wind speeds based upon the augmented data are compared with the original series. Based upon the results, predictions of the 50- and 100-year extreme wind speeds are then made.

Poisson-Gumbel Model for Wind Speed Threshold Estimation of Maximum Wind Speed

Poisson-Gumbel joint distribution model uses maximum wind speed corresponding to multiple typhoons to construct sample sequence.Thresholds are usually used to filter sample sequences to make them more consistent with Poisson distribution.However,few studies have discussed the threshold setting and its impact on Poisson-Gumbel joint distribution model.In this study,a sample sequence based on the data of Qinzhou meteorological station from 2005 to 2018 were constructed.We set 0%,5%,10%,20%and 30%gradient thresholds.Then,we analyzed the influence of threshold change on the calculation results of maximum wind speed in different return periods.The results showed that:(1)When the threshold increases,the maximum wind speed of each return period will decrease gradually.This indicates that the length of the sample series may have a positive effect on the return period wind speed calculation in Gumbel and Poisson-Gumbel methods.Although the augment of the threshold increases the average value of the maximum wind speed of the sample sequence,it shortens the length of the sample sequence,resulting in a lower calculated value of the maximum wind speed.However,this deviation is not large.Taking the common 10%threshold as an example,the maximum wind speed calculation deviation in the 50 a return period is about 1.9%;(2)Theoretically,the threshold is set to make the sample sequence more consistent with Poisson distribution,but this example showed that the effect is worth further discussion.Although the overall trend showed that the increase of the threshold can makeχ2 decrease,the correlation coefficient of linear fitting was only 0.182.Taking Qinzhou meteorological station data as an example,theχ2 of 20%threshold was as high as 6.35,meaning that the selected sample sequence was not ideal.

Extreme Values of Wind Speed over the Kara Sea Based on the ERA5 Dataset

Extreme values of wind speed were studied based on the highly detailed ERA5 dataset covering the central part of the Kara Sea. Cases in which the ice coverage of the cells exceeded 15% were filtered. Our study shows that the wind speed extrema obtained from station observations, as well as from modelling results in the framework of mesoscale models, can be divided into two groups according to their probability distribution laws. One group is specifically designated as black swans, with the other referred to as dragons (or dragon-kings). In this study we determined that the data of ERA5 accurately described the swans, but did not fully reproduce extrema related to the dragons;these extrema were identified only in half of ERA5 grid points. Weibull probability distribution function (PDF) parameters were identified in only a quarter of the pixels. The parameters were connected almost deterministically. This converted the Weibull function into a one-parameter dependence. It was not clear whether this uniqueness was a consequence of the features of the calculation algorithm used in ERA5, or whether it was a consequence of a relatively small area being considered, which had the same wind regime. Extremes of wind speed arise as mesoscale features and are associated with hydrodynamic features of the wind flow. If the flow was non-geostrophic and if its trajectory had a substantial curvature, then the extreme velocities were distributed according to a rule similar to the Weibull law.

Urban Wind Speed Analysis in Global Climate Change Perspective: <i>Karachi as a Case Study</i>

It is now well known that coastal urban local climate has been showing changing pattern due to global climate change. This communication attempts to explore fluctuating pattern of urban average monthly wind speed during past 50 years (1961-2010). It shows peculiar results taking Karachi (24?53'N, 67?00'E), a coastal mega-city of Pakistan, as a case study. Mann-Kendall trend test shows that March, April and October and both summer and winter seasons show positive trends for the average monthly wind speed during the whole study period (1961-2010). For the earlier 25 years data, it has been found that January, March, May, August, November and December and annual wind speed data have shown the negative trends. Only summer season has shown the positive trend for the wind speed. Similarly, for the most recent 25 years data it has been found that January, February, March, April, May, June, October, November and December and annual and both summer and winter wind speed data have shown the positive trends showing some degree of change in wind speed pattern. Probabilistic analysis reveals that average monthly wind speed data sets follow lognormal, logistic, largest extreme value, and Weibull (two-and three-parameters) probability distributions. Change point analysis has also confirmed the change in the pattern of observed average monthly wind speed data near 1992. The analysis performed reveals the effect of global warming on the local urban wind speed which appears to be temporal non-stationary.

基于爬坡方向状态划分的MCMC风电功率序列建模方法

由于电网弃风或者灵活性资源不足往往发生在风电大量发电时,故提高风电时间序列模型对大出力状态的建模-抽样精度,有助于后续的电网灵活性资源相关研究。在传统马尔科夫链蒙特卡洛(MCMC)法和持续与波动蒙特卡罗(PV-MC)法基础上,提出一种考虑爬坡方向状态划分的改进方法,以更准确地描述风电出力连续爬坡至大出力状态的过程。该方法以累积分布概率而不是以功率大小均匀划分状态区间,使各个状态区间的样本分布更均匀,提高了风电时间序列模型对大出力状态的建模-抽样精度。通过算例比较所提方法、MCMC法及PV-MC法生成风电功率序列与历史数据的分布特性和统计特性指标,结果表明,所提方法的拟合度较好,且能够有效解决MCMC法和PV-MC法高出力、样本偏少的问题。

基于Nakagami分布的风速概率分布拟合研究

准确拟合风速概率分布对估算潜在风场所蕴含的风能具有重要作用。引入二参数Nakagami分布对美国西部涵盖沿海、海岛和内陆的八个站点风速数据进行拟合,并对比瑞丽分布、伽马分布、威布尔分布、对数正态分布、广义极值分布以及JohnsonSB分布,使用决定系数、均方根误差、误差平方和以及Kolmogorov-Smirnov四个拟合指标进行校验。与参数较少的瑞丽分布、伽马分布、对数正态分布以及威布尔分布精度进行比较,Nakagami分布在八个站点的四个拟合指标均值中取得最优的拟合精度。与三参数的广义极值分布以及四参数JohnsonSB分布进行拟合对比,Nakagami分布在四个站点获得最优精度。根据风速数据的统计结果,当站点的平均风速低于3 m/s时,Nakagami比其它分布获得了更高的拟合精度。综合考虑Nakagami简便的计算及较高的拟合精度,其在拟合风速概率分布领域更具优势。

台风精细化风雨联合概率分布模型及输电线路失效概率评估

我国东南沿海地区经常发生台风致输电线路倒塌事故,大部分研究将倒塌原因归结于强风作用,而台风登陆常伴随持续的降雨,应同时考虑风和降雨的影响。该研究考虑台风结构不同区域的气象学特点,基于“山竹”、“海高斯”、“查帕卡”3组台风实测数据,根据数据采集时刻气象站所处台风区域(台风眼、螺旋雨带、外围大风区)将所有数据划分成3组。通过相关性分析揭示了风速和降雨强度的耦联关系,进而建立了不同台风区域的精细化风速-降雨强度联合概率分布模型。最后通过某输电线路开展了算例分析,计算了其在不同台风区域内的失效概率。结果表明,提出的精细化风雨联合概率模型能准确地描述风雨联合作用,对工程结构设计和性能评估具有重要参考价值。

基于TOPSIS法的风速概率分布评价方法

将风速概率分布评价视为多属性决策问题,基于逼近理想解排序法(TOPSIS)评价各类候选概率分布拟合风速概率分布的优度。首先,将候选概率分布和拟合优度指标分别类比为方案和属性;其次,根据相对贴近度量化候选概率分布与正理想解的差距,进而在计及指标冲突的情况下评价风速概率分布;最后,收集美国某两处观测站实测风速数据验证所述方法的适用性和准确性,结果表明:该方法能有效整合多种拟合优度指标,进而在不同风况下为特定观测站评价风速概率分布。

基于改进自适应蚁群算法的无人帆船路径规划

针对特定环境下的无人帆船路径规划问题,考虑到无人帆船复杂运动特性及目前路径评价指标较为单一等问题,本文提出了一种基于改进自适应蚁群算法的无人帆船路径规划方法。通过对角线优先规则解决蚁群前期寻路的盲目性问题,加快最优路径收敛速度;结合无人帆船在特定风向风速下的航速大小设计启发函数,并在转移概率中增加目标终点对无人帆船的吸引作用,使无人帆船最优路径评价指标更加贴合实际应用;引进不同自适应调整策略分别改进蚁群算法信息素更新机制和搜索步长,达到平衡全局搜索和局部搜索的目的,有效避免无人帆船路径陷入局部最优。本文通过多组仿真对比实验,验证了所提算法的合理性与优越性。

基于Vine Copula函数的风浪要素联合概率分布模型

随着全球气候变暖的加剧,极端气候现象发生的频率和强度均可能加大,这对海岸和近海结构的安全不利。基于中国东海的连云港海洋观测站实测风浪数据和Vine Copula理论,建立风浪要素中风速、波高、波浪周期、风向和波向五维随机变量之间的联合概率分布模型。采用极大似然法确定各风浪要素边缘分布模型参数,通过AIC信息准则和均方根误差RMSE进行拟合优度评价,由此建立风浪要素的边缘分布。采用带有基于残差的高斯似然函数的贝叶斯框架估计二维Copula函数的参数,结合AIC信息准则进行拟合优度评价并确定最优Copula函数。绘制最优联合分布概率密度图,与二维频率直方图进行对比以评价模型效果。采用Vine Copula函数建立多维联合概率模型并结合AIC值评价其拟合优度。研究结果表明:建立的Vine Copula模型可以较好地刻画风速、波高、波浪周期、风向和波向五维随机变量之间的联合概率分布。

G7高速公路下涝坝互通路基风吹雪研究

为了解高速公路互通立交路段路基的风吹雪特征并为后期挡雪设施提供理论依据,通过风场监测、现场积雪调查和数值模拟等方法,对新建京新高速公路(G7)巴里坤至木垒下涝坝段互通路基的风速风向及积雪分布特征进行了研究。风场监测结果表明,该路段风向不稳定,优势风向为西北风;数值模拟结果表明,互通路段相邻两匝道路基高差和间距影响下风侧路基的风速和积雪分布,当路基间距和高差分别为10 m、2 m和50 m、7 m时,下风侧路基风速相比主线路基分别下降了约35%和53%,路面积雪分别增加了14.0%和18.5%。积雪调查显示匝道积雪规律与数值模拟结果相符,应视下风侧匝道积雪情况在互通外侧合理布设防雪设施。

基于受限玻尔兹曼机和粗糙集的风速区间概率预测模型

针对风速的不确定性、时变和非线性特征,提出一种用于风速预测的基于受限玻尔兹曼机和粗糙集理论的区间概率分布学习(Interval Probability Distribution Learning, IPDL)模型。该模型包含一组区间隐藏变量,利用Gibbs抽样和对比散度来获取风速的概率分布,结合模糊Ⅱ型推理系统(Fuzzy Type Ⅱ Inference System, FT2IS),设计一个有监督回归的实值区间深度置信网络(Interval Deep Belief Network, IDBN)。算例结果表明,该方法结合了IPDL和FT2IS的鲁棒性,风速预测性能较好。

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。

风电场风速概率分布参数计算新方法

风能资源评估关系到风电的经济性和开发价值,一个地区的风速概率分布是该地区风能资源状况的最重要指标之一。在认为风电场风速服从双参数韦布尔(Weibull)分布前提下,为了提高参数计算精度,从智能化的角度提出尝试采用改进的微粒群算法对Weibull双参数进行建模和优化。由此参数估算能直接反映出风能资源特性的风能特征指标,与由常规最小二乘法、丹麦WAsP软件以及历史风速数据序列所计算的结果相比,实验表明该方法拟合精度更高,更接近实际风速状况,为风电场规划设计提供了更具价值的参考。

风速与风电功率的联合条件概率预测方法

采用确定性预测模型对风速和风电功率进行预测,无法传递结果的概率可信程度,不适应风险分析与调控应用的需要。为此,建立了以当前时段实测风速和下一时段预报风速为联合条件的离散预报误差概率统计(forecast errorprobability distribution,FEPD)模型,并以该模型对未来时段的预报误差概率分布进行预测。首先由历史统计结果确定修正因子,利用风速波动分布特征(speed disturbanceprobability distribution,DPD)对预报误差概率分布进行偏度修正,再将修正后的预报误差概率分布与由确定性预测算法得到的结果相结合,从而得到风速概率性预测结果。实例表明,所提方法可以较好地预测未来时段风速及风电功率变化的概率分布结果。

基于健康监测的苏通大桥风速风向联合分布研究

为了描述苏通大桥桥址区风速风向联合分布规律,为大跨度桥梁结构的风振分析精细化研究奠定基础,基于苏通大桥结构健康监测系统开展了桥址区风速风向联合分布研究。根据该系统风速仪在主梁跨中上游、跨中下游、北塔塔顶、南塔塔顶4个关键部位记录的风特性数据,采用数理统计方法,推算了4个部位10年、100年重现期的极值风速。结果表明,苏通大桥风特性监测数据合理可靠、桥址区极值风速分布概型接近Weibull分布,考虑风向影响的极值风速总体上比不考虑风向影响时的结果要小。