考虑时空相关性的风电机组风速清洗方法  

DATA CLEANING METHOD CONSIDERING TEMPORAL ANDSPATIAL CORRELATION FOR MEASURED WIND SPEED OFWIND TURBINES

作　　者：李莉 梁袁[1,2] 林娜 阎洁 孟航 刘永前[1,2] Li Li;Liang Yuan;Lin Na;Yan Jie;Meng Hang;Liu Yongqian(State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(North China Electric Power University),Beijing 102206,China;School of New Energy,North China Electric Power University,Beijing 102206,China)

机构地区：[1]新能源电力系统全国重点实验室(华北电力大学),北京102206 [2]华北电力大学新能源学院,北京102206

出　　处：《太阳能学报》2024年第6期461-469,共9页Acta Energiae Solaris Sinica

基　　金：国家重点研发计划“可再生能源与氢能技术”专项“风力发电复杂风资源特性研究及其应用与验证”项目(2018YFB1501100)。

摘　　要：为获得完整可靠的风速数据,提出一种考虑时空相关性的风电机组机舱风速清洗方法。利用图卷积神经网络(GCN)提取风速的空间相关信息、利用双向长短期记忆神经网络(Bi-LSTM)提取时间相关信息,建立GCN-LSTM模型重构各机组风速序列,实现对异常风速数据的识别和清洗。分析风速的时空特性及其对模型清洗精度的影响,确定最优时间尺度和机组节点数量2个重要的建模参数;以中国4个不同地形风电场为例对GCN-LSTM模型进行验证,结果表明考虑时空相关性可有效提高风速清洗精度,风速的时空相关性越高风速清洗误差越小,且该模型在不同地形风电场的风速清洗中表现出良好的鲁棒性。To obtain reliable and accurate wind speed data,a data cleaning method for measured wind speed of wind turbines was proposed in this study.The method incorporates spatiotemporal correlation by utilizing a graph convolutional neural network(GCN)to extract spatial correlation information and a bidirectional long short-term memory neural network(Bi-LSTM)to extract temporal correlation information.A GCN-LSTM model was established to reconstruct the wind speed of each wind turbine,so as to realize identification and removal of abnormal wind speed.The study also analyzes the spatiotemporal characteristics of wind speed and their impact on the accuracy of the proposed model.Two important modeling parameters are identified:the optimal time scale and the number of wind turbines.The proposed method was validated by using data from four wind farms with different terrains in China.The results show that incorporating spatiotemporal correlation can effectively improve accuracy of data cleaning.Moreover,the higher the spatiotemporal correlation of wind speed,the smaller the cleaning error.The proposed model has robustness in cleaning wind speed data under various terrain types.

关 键 词：风电场 风电机组 图神经网络 长短期记忆神经网络 风速时空相关性 数据清洗 

分 类 号：TK81[动力工程及工程热物理—流体机械及工程]