Variational Reconstruction and Simulation Experiments of Sea Surface Wind Field for Ocean Data Buoy

The sea surface wind field is an important physical parameter in oceanography and meteorology.With the continuous refinement of numerical weather prediction,air-sea interface materials,energy exchange,and other studies,three-dimensional(3D)wind field distribution at local locations on the sea surface must be measured accurately.The current in-situ observation of sea surface wind parameters is mainly achieved through the installation of wind sensors on ocean data buoys.However,the results obtained from this single-point measurement method cannot reflect wind field distribution in a vertical direction above the sea surface.Thus,the present paper proposes a theoretical framework for the optimal inversion of the 3D wind field structure variation in the area where the buoy is located.The variation analysis method is first used to reconstruct the wind field distribution at different heights of the buoy,after which theoretical analysis verification and numerical simulation experiments are conducted.The results indicate that the use of variational methods to reconstruct 3D wind fields is significantly effective in eliminating disturbance errors in observations,which also verifies the correctness of the theoretical analysis of this method.The findings of this article can provide a reference for the layout optimization design of wind measuring instruments in buoy observation systems and also provide theoretical guidance for the design of new observation buoys in the future.

Wind field reconstruction for the dispersion modeling of accidental chemical spills on complex geometry

Chemical spills on complex geometry are difficult to model due to the uneven concentration distribution caused by air flow over ground obstacles. Computational fluid dynamics(CFD) is one of the powerful tools to estimate the building-resolving wind flow as well as pollutant dispersion. However, it takes too much time and requires enormous computational power in emergency situations. As a time demanding task, the estimation of the chemical spill consequence for emergency response requires abundant wind field information. In this paper, a comprehensive wind field reconstruction framework is proposed, providing the ability of parameter tuning for best reconstruction accuracy. The core of the framework is a data regression model built on principal component analysis(PCA) and extreme learning machine(ELM). To improve the accuracy, the wind field estimation from the regression model is further revised from local wind observations. The optimal placement of anemometers is provided based on the maximum projection on minimum eigenspace(MPME) algorithm. The fire dynamic simulator(FDS) generates high-resolution data of wind flow over complex geometries for the framework to be implemented. The reconstructed wind field is evaluated against simulation data and an overall reconstruction error of 9% is achieved. When used in real case,the error increases to around 12% since no convergence check is available. With parameter tuning abilities,the proposed framework provides an efficient way of reconstructing the wind flow in congested areas.

ANALYSES OF WIND STRUCTURE OF TYPHOON FUNG-WONG (2008) AND ITS RELATION TO PRECIPITATION REGION

Using real analysis data of 1°×1° resolution of the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR),the nondivergent wind component and irrotational wind component obtained by the harmonic-cosine(H-C) method,and the wind structure of Typhoon Fung-Wong (coded 0808 in China) in 2008 was analyzed. The results indicated that the irrotational component was advantageous over the total wind in reflecting both the changes in convergent height and the asymmetrical convergence of Fung-Wong. In Fung-Wong,the nondivergent component was larger than the irrotational component,but the latter was much more variable than the former,which was obtained only from the wind partition method. Further analyses on the irrotational component demonstrated that the location of the convergent center at lower levels was almost the same as the divergent center during the development of Fung-Wong,and its convergent level was high in its life cycle,with the most highest up to 400 hPa when it became stronger. After the typhoon landed in the provinces of Taiwan and Fujian,respectively,its convergent center at lower levels was slowly detached from the divergent center at high levels and the convergent height was also depressed from high levels to lower levels. Gradually,this weakened the intensity of Fung-Wong. This kind of weakening was slow and Fung-Wong maintained its circulation for a long time over land because of its very thick convergent height. Analyses on wind partitioning provided one possible explanation to why Fung-Wong stayed for a long time after it landed. Furthermore,the asymmetric vertical ascending motion was induced by the asymmetric convergence at lower levels. In general,when typhoons (such as Fung-Wong) land,the rainfall region coincides with that of the convergence region (indicated by the irrotational component at lower layers). This means that the possible rainfall regions may be diagnosed from the convergent area of the irrotational component. For an observational experiment on typhoons,the convergent region may be considered as a key observational region.

Application of the nonlinear time series prediction method of genetic algorithm for forecasting surface wind of point station in the South China Sea with scatterometer observations

The present work reports the development of nonlinear time series prediction method of genetic algorithm（GA） with singular spectrum analysis（SSA） for forecasting the surface wind of a point station in the South China Sea（SCS） with scatterometer observations.Before the nonlinear technique GA is used for forecasting the time series of surface wind,the SSA is applied to reduce the noise.The surface wind speed and surface wind components from scatterometer observations at three locations in the SCS have been used to develop and test the technique.The predictions have been compared with persistence forecasts in terms of root mean square error.The predicted surface wind with GA and SSA made up to four days（longer for some point station） in advance have been found to be significantly superior to those made by persistence model.This method can serve as a cost-effective alternate prediction technique for forecasting surface wind of a point station in the SCS basin.

Partition airflow varying features of chaos-theory-based coalmine ventilation system and related safety forecasting and forewarning system

To realize real-time monitoring and short-term forecasting and forewarning of coalmine ventilation systems(CVS), in this paper, we first established a joint surface and underground CVS safety management system consisting of main ventilation fan, safety-partition linked passageways, and air-required locations. We then applied chaos theory to identify the air quantity and gas concentration of underground partition boundaries, and adopted a fixed data quantity, multi-step progressive, weighted first-order local-domain method to setup a chaos prediction model and a CVS safety forecasting and forewarning system formed by the normal change level, orange forewarning level, and red alarm level. We next conduct the on-field application of the system in a coalmine in Jining, Shandong, China. The results showed that (1) in the statistical scale of 5 min, the changes in both air quantity and gas concentration along CVS partition airflow boundaries were characteristic of chaos and could be used for short-term chaos prediction, and the latter was more chaotic than the former;(2) the setup chaos prediction model had a higher prediction precision and the established safety prediction system could not only predict the variation in CVS stability but also reflect the rationality of underground mining intensity. Thus, this CVS safety forecasting and forewarning system is of better application value.

Effects of Vertical Wind Shear on the Pre-Summer Heavy Rainfall Budget:A Cloud-Resolving Modeling Study

This study investigates the effects of vertical wind shear on the torrential rainfall response to the large-scale forcing using a rainfall separation analysis of a pair of two-dimensional cloud-resolving model sensitivity experiments for a pre-summer heavy rainfall event over southern China from 3-8 June 2008 coupled with National Centers for Environmental Prediction(NCEP)/Global Data Assimilation System(GDAS) data.The rainfall partitioning analysis based on the surface rainfall budget indicates that the exclusion of vertical wind shear decreases the contribution to total rainfall from the largest contributor,which is the rainfall associated with local atmospheric drying,water vapor divergence,and hydrometeor loss/convergence,through the reduction of the rainfall area and reduced rainfall during the rainfall event.The removal of vertical wind shear increases the contribution to total rainfall from the rainfall associated with local atmospheric drying,water vapor convergence,and hydrometeor loss/convergence through the expansion of the rainfall area and enhanced rainfall.The elimination of vertical wind shear enhances heavy rainfall and expands its area,whereas it reduces moderate rainfall and its area.

A Reconstructed Wind Stress Dataset for Climate Research over the Tropical Pacific during a 153-Year Period

There are close relationships between the sea surface temperature (SST) and the surface wind over the tropical Pacific.To study the past climate variability over the tropical Pacific,the long-term monthly wind stress anomalies over the tropical Pacific for the period of 1856–2008 are reconstructed with an SVD (singular value decomposition)-based statistical atmospheric model,where the wind stress anomalies are slave and directly correspond to the SST anomalies.The verification results show that the reconstructed wind stress data have high correlations and a small root mean square (RMS) error with the three reanalysis/simulated surface wind datasets from the last 50 years.In addition,the simulated SST anomalies from an intermediate oceanic model (IOM),which is forced by the reconstructed wind stress,can simulate the realistic interannual and decadal variability of the ENSO (El Nio-Southern Oscillation);this indicates that this new long-term wind stress dataset is useful for various climate studies,especially for the large-scale interannual and decadal variability.

Meshless Surface Wind Speed Field Reconstruction Based on Machine Learning

We propose a novel machine learning approach to reconstruct meshless surface wind speed fields,i.e.,to reconstruct the surface wind speed at any location,based on meteorological background fields and geographical information.The random forest method is selected to develop the machine learning data reconstruction model(MLDRM-RF)for wind speeds over Beijing from 2015-19.We use temporal,geospatial attribute and meteorological background field features as inputs.The wind speed field can be reconstructed at any station in the region not used in the training process to cross-validate model performance.The evaluation considers the spatial distribution of and seasonal variations in the root mean squared error(RMSE)of the reconstructed wind speed field across Beijing.The average RMSE is 1.09 m s^(−1),considerably smaller than the result(1.29 m s^(−1))obtained with inverse distance weighting(IDW)interpolation.Finally,we extract the important feature permutations by the method of mean decrease in impurity(MDI)and discuss the reasonableness of the model prediction results.MLDRM-RF is a reasonable approach with excellent potential for the improved reconstruction of historical surface wind speed fields with arbitrary grid resolutions.Such a model is needed in many wind applications,such as wind energy and aviation safety assessments.

贝壳形屋盖风压系数密度峰值聚类分区研究

鉴于贝壳形屋盖表面风压分布的特殊性,提出基于快速搜索技术的密度峰值聚类风压系数分区方法。以某贝壳形屋盖表面风压分布试验结果为基础,进行密度峰值聚类风压系数分区,采用SD有效性指标验证分区数的合理性,并与改进k-means聚类分区结果进行对比。结果表明:密度峰值聚类风压系数分区以风压系数相对距离、局部密度和综合指数为特征参数,能较好反映屋面风压分布特性,有效保证类聚合性和类类分离性;相较于改进k-means分区法,不同风向角下密度峰值聚类得到的分区数与SD最优聚类数接近;密度峰值聚类分区结果能更准确反映贝壳形屋盖表面风压分布特性,充分体现测点风压系数局部密度和相对距离特征值较大的基本原则,对于贝壳形屋盖的风压系数分区具有更好的适用性;贝壳形屋盖密度峰值聚类分区最大负风压系数在-0.59~-1.74之间。

基于球面角点特征的鱼眼相机与激光雷达联合标定方法

针对鱼眼相机和激光雷达的联合标定问题,提出一种基于球面角点特征的鱼眼相机与激光雷达联合标定方法。首先利用静电斥力物理模型得到了均匀分布在球面上的点集,并以这些点集为生长点对球面进行Voronoi划分,得到了均匀的球面像素,实现了鱼眼相机图像在球面上的均匀重采样;之后在球面上提取了鱼眼相机图像的角点特征,有效地避免了图像畸变带来的角点提取困难的问题;最后利用17组标定数据在球面上实现了鱼眼相机和激光雷达联合标定,在鱼眼图像上的重投影误差小于2个像素,并通过数据融合实验复现了真实场景中的彩色三维效果。

基于裕度因子的多风电场站集群频率协调控制策略

为提升高比例电力电子装备电力系统的涉网频率性能,提出了一种基于裕度因子的多风电场站集群频率协调控制方法。对于多风电场站集群系统一次调频功率的分配问题,综合考虑转子转速及变流器容量运行条件后,利用等比例裕度因子的一次调频功率分配方法,将有功功率指令下发给各风电场站。针对各风电场站中虚拟惯量、虚拟阻尼系数的制定问题,研究了一种基于可释放动能的频率调节参数分区设置方法。将各风电场站运行状态通过频率响应的变化趋势进行分区,并在此基础上结合可用调频动能对频率调节参数进行合理灵活的设置。仿真结果表明所提方法充分利用了各风电场站的调频资源,提高了高比例电力电子装备电力系统的频率稳定性,为新型电力系统的频率安全性提供了相应的技术保障。

无人机序列影像快速三维重建方法研究

近几年来,基于无人机倾斜摄影影像的三维重建技术已是摄影测量与计算机视觉领域的研究热点,已被广泛应用于大比例尺地形图生产以及智慧城市等领域。针对无人机倾斜影像的数据高冗余、时间与空间复杂度高等问题,本文提出了一种快速有效的无人机序列影像三维重建方法。选择南水北调中线工程开放场景某一段的序列无人机影像,在不辅助POS等其他定位信息的基础上,仅依靠重叠影像间的匹配结果划定影像分区;在单独分区内采用改进的增量式SFM方法进行三维重建;最后再将各个分区依据分区间的重叠信息进行合并,完成整个测区的三维重建,并与商业建模软件以及传统的计算机视觉三维重建方法对比。结果表明,该方法在保持高精度的同时可以大幅提高重建效率,重建速度可提升5倍以上,尤其适用于大场景大规模数据集。

海上风力机前端风电场瞬态重构研究

针对海上风电单机位风速测点有限的关键问题,提出一种基于少数风速测点的海上风力机前端风电场瞬态扩展方法。基于本征正交分解(POD)将先验风电场数据分解为时间系数与空间模态特征信息,通过循环神经网络(RNN)建立有限风速测点到全局风电场的非线性映射关系,实时构建海上瞬态风电场。结果表明基于POD-RNN的重构模型可通过有限风速测点准确重构海上风力机前端风电场,全局风电场瞬态重构均方根误差(RMSE)可控制在1.8136 m/s内。

小容量换装大容量风电机组的基础升级改造分析

为将运行中的小容量风力发电机组置换为大容量机组,须对其基础进行改造加固,通过扩大基础承台、增加桩基数量的方式,对原基础进行改造,使其达到新机组的承载要求。该技术方案的应用,为后续类似改造项目提供借鉴。

基于EEMD-PSO-ELM的风电功率超短期预测

针对风电场功率不稳定特性引起风电功率预测精度不高的问题,提出1种基于EEMD-PSO-ELM的超短期风电功率预测方法。首先,采用集合经验模态分解(ensemble empirical mode decomposition,EEMD)将风电功率序列分解为若干个模态,从而避免了模态混叠;其次,利用相空间重构对分解得到的模态计算Hurst指数,并依据Hurst指数得到最优子序列;最后,采用粒子群算法(particle swarm optimization,PSO)-极限学习机(extreme learning machine,ELM)模型对最优子序列风电功率进行预测。以某风电场为例,采用预测模型进行分析,实验结果表明EEMD-PSO-ELM预测模型的风电功率预测精度更高。

基于最小二乘法的新能源出力时序数据重构方法

分析了风电和光伏的出力特性和相关性,进而得到新能源出力的概率模型。根据不同的数据样本,利用最小二乘法作为参数估计方法,计算得到概率分布模型参数。在此基础上,提出新能源出力时序数据重构的方法和数据重构步骤,为清洁能源的有序开发和发电并网提供参考。

风电场异常数据辨识与重构技术综述

风电场运行数据的质量是影响风电消纳的重要因素,电网调度运行以及电力市场等业务应用都需要风电场数据支撑,但一般情况下,风电场采集到的原始数据会包含部分异常数据。本文详细分析对比了国内外学者对于异常数据辨识和异常数据重构使用的方法。依据数据辨识原理将目前国内外使用的数据辨识方法分为3类,阐述了各辨识方法优越性以及局限性;其次,根据使用重构方法的数量将数据重构方法分为了单一重构法和复合重构法,阐述了各重构法的适用场合以及局限性;然后,根据风电数据的不同应用场合,对异常数据辨识对象以及辨识方法做了详细研究;最后对本领域未来可研究的问题进行了展望。

基于Copula理论的风光互补配网经济运行联合配网重构优化

文章建立了基于Copula理论的风光互补配电网经济运行模型,联合配网重构进行求解。分析了风光输出功率以及互补发电模型,考虑了Copula相关性函数对风电和光伏输出功率的影响。说明了风光互补相关性在经济运行联合配网重构优化模型中的应用原理,建立了以网损、运行成本、购电成本等最小为目标的多目标优化模型。提出采用改进的帕累托强度算法(SPEA2)进行求解,在仿真算例中说明了考虑相关性对结果的影响,验证了文章所提模型的有效性。

基于属性约简重构的自校正卷积记忆风速预测

考虑风速属性及时空相关性的预测建模是规模化风电并网的研究热点,该文基于属性约简重构提出一种自校正卷积记忆超短期预测模型。利用快速相关性滤波对风速序列关联属性进行排序筛选,据此改进K-mediods方法对风电场机群聚类,基于改进灰色关联度分析簇内风机的风速时空相关性,划分典型风机多阶邻域,并重构风速信息矩阵。然后,将重构的时空多维信息输入卷积双层记忆网络,通过卷积神经网络进行风速信息降维与空间特征提取,再由双层记忆神经网络进行多位置多步超短期预测,同时基于反向误差传播原理在记忆网络中引入自校正误差修正单元。最后对实际风电场的风速进行预测,验证所提方法的有效性。