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.

Wind speed model based on kernel density estimation and its application in reliability assessment of generating systems

An accurate probability distribution model of wind speed is critical to the assessment of reliability contribution of wind energy to power systems. Most of current models are built using the parametric density estimation(PDE) methods, which usually assume that the wind speed are subordinate to a certain known distribution(e.g. Weibull distribution and Normal distribution) and estimate the parameters of models with the historical data. This paper presents a kernel density estimation(KDE) method which is a nonparametric way to estimate the probability density function(PDF) of wind speed. The method is a kind of data-driven approach without making any assumption on the form of the underlying wind speed distribution, and capable of uncovering the statistical information hidden in the historical data. The proposed method is compared with three parametric models using wind data from six sites.The results indicate that the KDE outperforms the PDE in terms of accuracy and flexibility in describing the longterm wind speed distributions for all sites. A sensitivity analysis with respect to kernel functions is presented and Gauss kernel function is proved to be the best one. Case studies on a standard IEEE reliability test system(IEEERTS) have verified the applicability and effectiveness of the proposed model in evaluating the reliability performance of wind farms.

Probabilistic Model for Wind Speed Variability Encountered by a Vessel

As a result of social awareness of air emission due to the use of fossil fuels, the utilization of the natural wind power resources becomes an important option to avoid the dependence on fossil resources in industrial activities. For example, the maritime industry, which is responsible for more than 90% of the world trade transport, has already started to look for solutions to use wind power as auxiliary propulsion for ships. The practical installation of the wind facilities often requires large amount of investment, while uncertainties for the corresponding energy gains are large. Therefore a reliable model to describe the variability of wind speeds is needed to estimate the expected available wind power, coefficient of the variation of the power and other statistics of interest, e.g. expected length of the wind conditions favorable for the wind-energy harvesting. In this paper, wind speeds are modeled by means of a spatio-temporal transformed Gaussian field. Its dependence structure is localized by introduction of time and space dependent parameters in the field. The model has the advantage of having a relatively small number of parameters. These parameters have natural physical interpretation and are statistically fitted to represent variability of observed wind speeds in ERA Interim reanalysis data set.

Design Wind Speed Evaluation Technique in Wind Turbine Installation Point by Using the Meteorological and CFD Models

It is highly important in Japan to choose a good site for wind turbines, because the spatial distribution of wind speed is quite complicated over steep complex terrain. We have been developing the unsteady numerical model called the RIAM-COMPACT (Research Institute for Applied Mechanics, Kyushu University, Computational Prediction of Airflow over Complex Terrain). The RIAM-COMPACT is based on the LES (Large-Eddy Simulation). The object domain of the RIAM-COMPACT is from several m to several km, and can predict the airflow and gas diffusion over complex terrain with high precision. In the present paper, the design wind speed evaluation technique in wind turbine installation point by using the mesoscale meteorological model and RIAM-COMPACT CFD model was proposed. The design wind speed to be used for designing WTGs can be calculated by multiplying the ratio of the mean wind speed at the hub-height to the mean upper-air wind speed at the inflow boundary, i.e., the fractional increase of the mean hub-height wind speed, by the reduction ratio, R. The fractional increase of the mean hub-height wind speed was evaluated using the CFD simulation results. This method was proposed as Approach 1 in the present paper. A value of 61.9 m/s was obtained for the final design wind speed, Uh, in Approach 1. In the evaluation procedure of the design wind speed in Approach 2, neither the above-mentioned reduction rate, R, nor an upper-air wind speed of 1.7 Vo, where Vo is the reference wind speed, was used. Instead, the value of the maximum wind speed which was obtained from the typhoon simulation for each of the investigated wind directions was adopted. When the design wind speed was evaluated using the 50-year recurrence value, the design wind speed was 48.3 m/s. When a somewhat conservative safety factor was applied, that is, when the 100 year recurrence value was used instead, the design wind speed was 52.9 m/s.

Performance analysis of 20 Pole 1.5 KW Three Phase Permanent Magnet Synchronous Generator for low Speed Vertical Axis Wind Turbine

This paper gives performance analysis of a three phase Permanent Magnet Synchronous Generator (PMSG) connected to a Vertical Axis Wind Turbine (VAWT). Low speed wind condition (less than 5 m/s) is taken in consideration and the entire simulation is carried in Matlab/Simulink environment. The rated power for the generator is fixed at 1.5 KW and number of pole at 20. It is observed under low wind speed of6 m/s, a turbine having approximately1 mof radius and2.6 mof height develops 150 Nm mechanical torque that can generate power up to 1.5 KW. The generator is designed using modeling tool and is fabricated. The fabricated generator is tested in the laboratory with the simulation result for the error analysis. The range of error is about 5%-27% for the same output power value. The limitations and possible causes for error are presented and discussed.

Analysis of Observed and Modelled Near-Surface Wind Extremes over the Sub-Arctic Northeast Pacific

Wind speed extremes in the sub-Arctic realm of the North-East Pacific region were investigated through extreme value analysis of wind speed obtained from wind simulations of the COSMO-CLM (Consortium for Small-scale Modelling, climate version) mesoscale model, as well as using observed data. The analysis showed that the set of wind speed extremes obtained from observations is a mixture of two different subsets each neatly described by the Weibull distribution. Using special metaphoric terminology, they are labelled as “Black Swans” and “Dragons”. The “Dragons” are responsible for strongest extremes. It has been shown that both reanalysis and GCM (general circulation model) data have no “Dragons”. This means that such models underestimate wind speed maxima, and the important circulation process generating the anomalies is not simulated. The COSMO-CLM data have both “Black Swans” and “Dragons”. This evidence provides a clue that an atmospheric model with a detailed spatial resolution (we used in this work the data from domain with 13.2 km spatial resolution) does reproduce the special mechanism responsible for the generation of the largest wind speed extremes. However, a more thorough analysis shows that the differences in the parameters of the cumulative distribution functions are still significant. The ratio between the modelled Dragons and Black Swans can reach up to only 10%. It is much less than 30%, which was the level established for observations.

An Extreme Value Analysis of Wind Speed over the European and Siberian Parts of Arctic Region

Multiyear observed time series of wind speed for selected points of the Arctic region (data of station network from the Kola Peninsula to the Chukotka Peninsula) are used to highlight the important peculiarities of wind speed extreme statistics. How largest extremes could be simulated by climate model (the INM-CM4 model data from the Historical experiment of the CMIP5) is also discussed. Extreme value analysis yielded that a volume of observed samples of wind speeds are strictly divided into two sets of variables. Statistical properties of one population are sharply different from another. Because the common statistical conditions are the sign of identity of extreme events we therefore hypothesize that two groups of extreme wind events adhere to different circulation processes. A very important message is that the procedure of selection can be realized easily based on analysis of the cumulative distribution function. The authors estimate the properties of the modelled extremes and conclude that they consist of only the samples, adhering to one group. This evidence provides a clue that atmospheric model with a coarse spatial resolution does not simulate special mechanism responsible for appearance of largest wind speed extremes. Therefore, the tasks where extreme wind is needed cannot be explicitly solved using the output of climate model. The finding that global models are unable to capture the wind extremes is already well known, but information that they are members of group with the specific statistical conditions provides new knowledge. Generally, the implemented analytical approach allows us to detect that the extreme wind speed events adhere to different statistical models. Events located above the threshold value are much more pronounced than representatives of another group (located below the threshold value) predicted by the extrapolation of law distributions in their tail. The same situation is found in different areas of science where the data referring to the same nomenclature are adhering to different statistical models. This result motivates our interest on our ability to detect, analyze, and understand such different extremes.

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.

The Control System Simulation of Variable-Speed Constant-Frequency Wind Turbine

In general,Variable-Speed Constant Frequency (VSCF)Wind generation system is controlled by stator voltage orientation method which based on the mathematic model of VSCF Wind generation system and discussed the control strategy.Present the whole dynamic control model of variable-speed wind generator system in MATLAB/ Simulink,and the simulation results confirm the validity and effectiveness of the proposed control strategy.

Asymptotic Tracking Control for Wind Turbines in Variable Speed Mode

This paper presents a variable speed control strategy for wind turbines in order to capture maximum wind power.Wind turbines are modeled as a two-mass drive-train system with generator torque control.Based on the obtained wind turbine model,variable speed control schemes are developed.Nonlinear tracking controllers are designed to achieve asymptotic tracking for a prescribed rotor speed reference signal so as to yield maximum wind power capture.Due to the difficulty of torsional angle measurement,an observer-based control scheme that uses only rotor speed information is further developed for global asymptotic output tracking.The effectiveness of the proposed control methods is illustrated by simulation results.

基于LBM的典型建筑布局树木风环境模拟

在建筑方案设计阶段定量评估景观绿植对建筑风环境影响,对于提高行人区域热舒适性具有重要实践意义。结合格子玻尔兹曼计算流体力学方法和多孔介质模型,开展了边界层流场-建筑-树木耦合模拟和关键参数验证,针对城市街道区域典型矩形建筑平面布局存在的风速加速现象,开展树木挡风效应分析。研究结果表明:模拟方法能够重现出与试验吻合一致的树木尾流区平均风速和湍流特征;基于不同阻力系数的树木透孔率对树木背风区下游1H~3H范围内挡风效应影响明显;基于等效原则提出的圆柱体简化模型能够计算出与树木原型一致的挡风效应变化规律;建筑两侧和迎风端布置合理间距的树木能够减小矩形建筑两侧风速加速比和加速区域面积,可以通过经济合理的树木布置方案实现建筑周围风环境舒适度优化和通风廊道畅通。研究成果可应用于景观植被风环境优化设计,为方案设计阶段绿色建筑自然通风评估提供新的计算参考。

基于多变量灰色模型的台风强度模拟方法

随着台风全路径模拟技术的不断完善,全海域、大范围的台风危险性分析方法得到发展,然而强度模型的模拟精度不能满足需求。针对台风强度演变受多因素影响、动力关系复杂及强度变化随机性强的特点,基于多变量灰色模型对台风强度模拟方法进行研究,并建立起台风多变量灰色强度模型。利用该模型还原了台风“山竹”的强度演变过程,同时对西北太平洋地区整体台风强度进行模拟及检验分析。结果表明:垂直风切变及垂直速度与台风强度的灰色关联度分别为0.771 8、0.745 1,显著高于其他3个环境因素;台风“山竹”强度模拟的后验差比值及小概率误差分别为0.349 1、0.960 8,达到了最高模拟精度;模型模拟所得西北太平洋地区整体的强度变化趋势及分布特征均与地区实际情况保持一致。

大型低速风洞全模阵风试验支撑装置研制与验证

为适应日益增多的低速风洞全模阵风试验的需要,发展阵风试验技术,在航空工业气动院FL-10风洞研制了一套双自由度支撑装置,该装置放开了模型升沉和俯仰方向的较大运动自由度,实现了飞机刚体运动模态的模拟。装置主要结构为“钢梁+小滑车”,小滑车可在钢梁上自由滑动,避免了采用滑轨形式导致模型运动过程中出现卡滞现象;采用整流翼型与风洞上下壁板连接,减小了对风洞的破坏,降低了对风洞流场的影响;装置升沉运动高度为3 m,俯仰角范围可达±34°;升沉摩擦因数小,机构变形量小,具备模型防护功能。应用该装置成功开展了民机阵风载荷减缓试验,证明了装置设计合理,可以应用于低速全模阵风试验。

基于D3AR的半球共形阵低空风切变风速估计方法

针对半球共形阵体制下进行低空风切变检测时会受到强地杂波信号的干扰,导致风切变信号难以检测的问题,提出了一种基于空时自回归的直接数据域算法(Space-Time Autoregressive Direct Data Domain,D3AR)的低空风切变风速估计方法。该方法首先将待检测距离单元的数据从空域、时域以及空时域进行信号对消处理;然后将处理后的数据矩阵描述为空时自回归(Autoregression,AR)模型并估计模型参数;再通过构造与杂波子空间正交的空间来实现对杂波的抑制,最后通过提取待检测单元的最大多普勒频率来估计风场速度。根据仿真结果显示,该方法有效地实现了地杂波抑制,并且能够精确估计风速。

基于频域分析的高速列车侧风倾覆机理

侧风作用下列车的动态环境以轮轨相互作用为主向,以空气动力作用为主演变,列车的侧风倾覆行为成为威胁列车行车安全性的主要诱因.首先,采用精细化车-轨耦合模型开展列车侧风倾覆的频域特性分析,以明确侧风倾覆响应对列车模型的敏感性;基于考虑模态特性的频域分析框架,推导脉动风及轨道不平顺与列车倾覆动力响应间的传递函数,结合相应参数进行分析,以直观揭示列车的侧风倾覆机理.结果表明:列车倾覆行为受绕车体下心侧滚模态和车体沉浮模态控制影响,其风荷载影响要明显大于轨道不平顺;在轨道不平顺激励下,第一阶模态贡献主要由轨向不平顺引起,第二阶模态贡献主要由高低不平顺引起,在脉动风荷载激励下,其顺风向脉动风分量起主要贡献;车速、风速和风向角的增大都会引起列车动力响应的增大,进而降低列车安全运营时的最大允许风速;失效概率的增大会降低动力响应的极值,进而提高安全运营风速.

外浮顶储油罐静储过程VOCs排放影响因素研究

为解决原油罐储过程中VOCs排放问题,从理论计算油田现场储罐进VOCs损耗量出发,定性分析大气温度、太阳辐射以及风速对储罐排放VOCs的影响,并基于不同影响因素对VOCs排放的影响方式给出解决办法。在原油外浮顶储罐静储过程VOCs排放的主要影响因素定性分析过程中,发现随着太阳辐射强度的增大、大气温度的升高和风速的增快,储罐VOCs损耗量也随之增加。考虑到太阳辐射、大气温度对储罐VOCs排放的影响,建议采用隔热涂层和加装反光条措施进行减排;在应对风速影响方面,采取消除液面上的气体空间和设置呼吸阀挡板进行减排。

典型峡谷Ⅴ型地貌下桥址区风特性

为得到山区峡谷典型Ⅴ型地形下的桥梁抗风设计的关键风特性参数,通过“数值风洞试验”建立模型,以实桥为工程背景构建三维地形网格,基于3种典型湍流模型和流体壁面粗糙度对桥位的风场特性进行分析,选取最优湍流模型和流体壁面粗糙度对该地形桥位处的风场特性进行分析;围绕两个影响桥址区风特性的地形参数(山体高度和夹角),阐明Ⅴ型峡谷地形下风特性参数的变化规律,并与现行规范进行对比,推算桥面设计基准风速。结果表明:该Ⅴ型峡谷地形的最佳湍流模型和流体壁面粗糙度分别为RNG k-ε和20 m;随着山体高度及山体夹角两参数的变化,风速增加幅度分别为2.37%~12.56%和1.24%~6.98%;山体高度及山体夹角两变参数下,梯度风高度分别为700 m和800 m左右;实际湍流强度大于规范中四类地表规范值,在离地高度40 m范围内,湍流强度更接近于D类地表。实际Ⅴ型峡谷桥址区不能简单按规范归为C类或D类地形,在设计风参数计算时应综合考虑规范和经验公式,还可借助模拟手段来提高其准确性。

带支撑广义Maxwell粘弹性阻尼耗能结构风振响应分析

工程中粘弹性阻尼器的安装通过支撑与结构进行连接,但在安装粘弹性阻尼器的耗能结构随机响应分析中,为了简化计算过程,将支撑的刚度看作无穷大或者忽略支撑刚度的影响。实际上,对支撑刚度的影响加以考虑更能符合工程实际。针对考虑支撑刚度影响的粘弹性阻尼耗能结构风振响应分析过程复杂的问题,提出了一种求解考虑支撑影响的广义Maxwell粘弹性阻尼耗能结构基于Davenport谱风振响应的简明解析解法。在广义Maxwell粘弹性阻尼器微分型本构模型基础上,给出了考虑支撑刚度的粘弹性阻尼器等效本构关系。将粘弹性阻尼器等效本构关系与结构运动方程联立,采用复模态法将其解耦,获得结构风振响应的统一表达式。将耗能结构在Davenport风速谱下的系列响应功率谱密度函数分解为频域响应函数与Davenport功率谱密度函数的乘积形式,基于随机振动理论中谱矩的定义,对响应功率谱密度函数积分后获得无积分项的系列响应谱矩表达式。在算例中通过与虚拟激励法计算结果对比,验证了所提解法的准确性,并分析了支撑刚度在耗能系统中的影响。

ML组合的CYGNSS海面风速反演质量控制模型

卷积神经网络(CNN)可用于气旋全球导航卫星系统(CYGNSS)的海面风速反演。虽然在模型训练前设置了质量控制指标来检测和削弱CYGNSS的异常观测数据,但CYGNSS观测数据中仍存在异常值导致模型反演精度降低,甚至出现错误反演结果。因此,提出一种基于机器学习(ML)组合的海面风速反演模型。在基于CNN回归模型的CYGNSS反演海面风速基础上,ML分类模型生成CNN回归结果的质量标志位,该标志位可以检测并删除CNN回归结果的异常值,进一步提高风速反演结果的数据质量,ML分类模型能够更好地考虑各种数据误差之间的相互作用,而不是单独使用每个条件的阈值,以达到更优的海面风速反演精度的效果。实验对比了Logistic回归(LR)、决策树(DT)、朴素贝叶斯模型、K最邻近(KNN)算法、神经网络(NN)模型、支持向量机(SVM)算法等6个分类模型,其中,基于KNN算法的分类模型对风速反演质量控制的效果最优。所提风速反演组合模型显著提高了反演结果的精度,在0~20 m/s区间内,异常样本过滤率为81.27%,在所有被过滤的数据中,过滤正确率为86.03%;风速反演误差的均方根误差从无ML分类模型的1.7 m/s降低到有ML分类模型的1.44 m/s,其中,训练样本为0~10 m/s的反演结果精度提升效果较为明显,证明了所提风速反演组合模型对风速质量控制的有效性。

基于机器学习模型FY⁃3D MWRI海面风速反演

风云三号D星(FY-3D)微波成像仪(MWRI)L1级亮温数据可用于全球海面风速反演,本文讨论了在晴空区和云区使用多元线性统计回归模型和机器学习模型反演海面风速的情况,在晴空区将4 d测试集分别放入多元线性统计回归模型,采用随机森林(RF),支持向量回归(SVR),卷积神经网络(CNN)和Stacking融合(SF)模型对海面风速进行反演,最优的均方根误差(RMSE)分别为1.56、1.31、1.24、1.29和1.27 m/s;在云区2 d测试集上的最优RMSE分别为2.12、1.98、1.87、1.89和1.89 m/s。为了进一步验证晴空区海面风速反演的可靠性,选取美国国家浮标数据中心(NDBC)实测的浮标风速对海面反演风速进行验证,CNN反演风速与NDBC实测风速的RMSE为0.74 m/s,决定系数(R^(2))为0.80;SF反演风速与NDBC实测风速的RMSE为0.85 m/s,R^(2)为0.74。结果证实了通过机器学习模型能够很好地完成FY-3D MWRI亮温反演全球海面风速的任务。