基于机群空间解耦信息聚合的风速超短期预测  

Ultra-short-term Wind Speed Prediction Based on Information Aggregation of Cluster Space Decoupling

作　　者：潘超[1] 孙惠 王超[1] 孟涛 PAN Chao;SUN Hui;WANG Chao;MENG Tao(Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology(Northeast Electric Power University),Ministry of Education,Jilin 132012,Jilin Province,China;State Grid Jilin Electric Power Research Institute Co.,Ltd.,Changchun 130021,Jilin Province,China)

机构地区：[1]现代电力系统仿真控制与绿色电能新技术教育部重点实验室(东北电力大学),吉林省吉林市132012 [2]国网吉林省电力科学研究院有限公司,吉林省长春市130021

出　　处：《电网技术》2025年第4期1393-1402,I0028,共11页Power System Technology

基　　金：国家重点研发计划项目(2022YFB2404000)。

摘　　要：准确的风速预测对于系统稳定和经济运行至关重要,文章提出了一种基于机群空间解耦信息聚合的卷积记忆超短期预测模型。首先,分析了机群受到的尾流效应影响,将风机尾流影响因子嵌入聚类分析,实现了基于风机尾流关联的空间解耦。然后,构建时空关联指标选取各解耦簇代表风机,并结合时序信息相似性扩展簇内时空信息域。基于高阶时空域聚合信息,构建了卷积记忆网络(convolutional memory network,CMEN)以增强时空特征,进行风速超短期预测。最后,将所提模型应用于实际风电场风速预测,并与实测数据对比分析,验证了所提模型有效性和适用性。Accurate wind speed prediction is essential for the stable and economical operation of the power system,and an ultra-short-term prediction model of convolutional memory network is proposed based on information aggregation of cluster space decoupling.Firstly,the influence of the wake effect of the cluster is analyzed,and the wake effect impact factor is embedded into cluster analysis to realize the space decoupling based on the wake correlation of the wind turbines.Then,the spatio-temporal correlation index is constructed.The representative wind turbine is selected from each decoupling cluster.And the spatio-temporal information domain is extended by combining temporal information similarity.Based on the aggregation information of the high-order spatiotemporal domain,the convolutional memory network is constructed to enhance the spatiotemporal characteristics and carry out ultra-short-term wind speed prediction.Finally,the proposed model is applied to the wind speed prediction of actual wind farm,the effectiveness and applicability of the model are verified through comparative analysis.

关 键 词：尾流 空间解耦 信息聚合 卷积记忆网络 

分 类 号：TM614[电气工程—电力系统及自动化]