利用多空间尺度下时空相关性的点云分布多风机风速预测  

Wind Speed Forecasting for Multiple Wind Turbines with Point Cloud Distribution Using Spatio-temporal Correlation on Multiple Spatial Scale

作　　者：王晨 寇鹏[1] 王若谷 高欣 WANG Chen;KOU Peng;WANG Ruogu;GAO Xin(School of Electrical Engineering,Xi′an Jiaotong University,Xi'an 710049,China;Electric Power Research Institute of State Grid Shaanxi Electric Power Corporation,Xi'an 710100,China)

机构地区：[1]西安交通大学电气工程学院,陕西省西安市710049 [2]国网陕西省电力公司电力科学研究院,陕西省西安市710100

出　　处：《电力系统自动化》2021年第22期65-73,共9页Automation of Electric Power Systems

基　　金：国家自然科学基金资助项目(52077165);中央高校基本科研业务费专项资金资助项目(xzy012019022)。

摘　　要：在风机呈不规则排列的风电场中,不同空间位置下的众多风机分布构成点云,而不是规则化的矩形网格。点云是不规则且无序的,可以代表任意风电场中多风机的地理位置分布,但是不能构成卷积神经网络(CNN)高度规则的网格输入,卷积算子难以学习其空间局部相关性。若直接将不规则点云映射为网格排列进行常规卷积,会失去点云原始的空间信息。为此,采用点CNN进行空间相关性提取,再利用简单循环单元进行时间相关性提取,从而获取点云数据的时空相关性。同时,设计点CNN时融入了多尺度下的空间特征提取与汇集。最后,结合实际以点云分布的多风机仿真结果验证了所提预测模型的有效性。In a wind farm where the wind turbines are arranged irregularly,the distribution of many wind turbines in different spatial locations forms a point cloud instead of a regular rectangular grid.The point cloud is irregular and unordered,which can represent the geographical location distribution of multiple wind turbines in any wind farm.However,the point cloud data cannot constitute the input of the highly regular grid for the convolutional neural network(CNN).It is difficult for the convolution operator to learn the spatial local correlation in the point cloud.If the irregular point cloud is directly mapped to a grid arrangement for regular convolution,the original spatial information of the point cloud will be lost.Therefore,the point CNN is used for spatial correlation extraction,then the simple recurrent unit is used to extract the temporal correlation information.And then the spatio-temporal correlation of point cloud data can be obtained.At the same time,the spatial features on multiple scales are extracted and integrated when designing the point CNN.Finally,the simulation results of multiple wind turbines with actual point cloud distribution show the effectiveness of the proposed forecasting model.

关 键 词：点云 多风机 本地风速预测 时空相关性 卷积神经网络 简单循环单元 

分 类 号：TM614[电气工程—电力系统及自动化]