Ultra-short-term Wind Speed Forecasting Based on Spatio-temporal Graph Memory of Clustering Wind Turbine Group Association Topology

被引：0
|
作者：
Pan C. [1 ]
Jiang D. [1 ]
Li B. [2 ]
Sun Y. [2 ]
Hao C. [2 ]
机构：
[1] Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Northeast Electric Power University, Ministry of Education, Jilin Province, Jilin
[2] State Grid Jilin Electric Power Co., Ltd., Jilin Province, Changchun
来源：
Dianwang Jishu/Power System Technology | 2023年 / 47卷 / 11期
关键词：
average wind speed of cluster; improved k-means clustering; optimal directed incidence topology; space-time graph memory network;
D O I：
10.13335/j.1000-3673.pst.2023.0418
中图分类号：
学科分类号：
摘要：
In order to improve the prediction accuracy and efficiency of the wind speed in large-scale wind farms, an optimal correlation topology of a clustering wind turbine group is proposed, and a spatio-temporal graph memory model is constructed to predict the wind speed. The average wind speed fluctuation characteristics of the cluster are analyzed, and the stability index is structured. Based on this, the k-means clustering is embedded to improve the intra-class wind speed complementarity. Combined with the mutual information quantitative analysis of the correlation between the sub-cluster fans, the optimal directed correlation topology is built. Based on the correlation topology and the wind speed correlation attributes, the spatial-temporal graph data set of the cluster wind speed is established by inputting the spatial-temporal graph memory network. The spatial features are extracted by the graph attention, and the time series information is processed by the memory network to output the ultra-short-term prediction results of the cluster average wind speed. Finally, the model is applied to the actual wind farm wind speed prediction, and the accuracy and effectiveness of the proposed method are verified through comparative analysis. © 2023 Power System Technology Press. All rights reserved.
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页码：4607 / 4618
页数：11
相关论文
共 34 条
  • [11] HU Shuai, XIANG Yue, SHEN Xiaodong, Wind power prediction model considering meteorological factor and spatial correlation of wind speed[J], Automation of Electric Power Systems, 45, 7, pp. 28-36, (2021)
  • [12] PAN Chao, LI Runyu, CAI Guowei, Wind speed prediction with self-tuning convolutional memory based on attribute reduction reconstruction[J], Proceedings of the CSEE, 43, 7, pp. 2721-2731, (2023)
  • [13] TANG Zhenhao, ZHAO Gengnan, Very short-term wind direction prediction via self-tuning wavelet long-short term memory neural network[J], Proceedings of the CSEE, 39, 15, pp. 4459-4467, (2019)
  • [14] Zhi LIU, WANG Jinmeng, Research on traffic accident prediction based on spatio-temporal graph convolutional network[J], Journal of Zhejiang University of Technology, 50, 2, pp. 128-135, (2022)
  • [15] ZHOU Fan, Qing YANG, ZHONG Ting, Variational graph neural networks for road traffic prediction in intelligent transportation systems[J], IEEE Transactions on Industrial Informatics, 17, 4, pp. 2802-2812, (2021)
  • [16] WU Qiang, ZHENG Hongling, Xiaozhu GUO, Promoting wind energy for sustainable development by precise wind speed prediction based on graph neural networks[J], Renewable Energy, 199, pp. 977-992, (2022)
  • [17] Mei YU, ZHANG Zhuo, Xuewei LI, Superposition Graph Neural Network for offshore wind power prediction[J], Future Generation Computer Systems, 113, pp. 145-157, (2020)
  • [18] YANG Peihong, HU Qinglin, FU Pan, The modeling of wind farm considering the changes of wind speed，wind direction and wake effect[J], Renewable Energy Resources, 34, 5, pp. 692-698, (2016)
  • [19] Yang CUI, MU Gang, LIU Yu, Spatiotemporal distribution characteristic of wind power fluctuation[J], Power System Technology, 35, 2, pp. 110-114, (2011)
  • [20] AL-WAKEEL A, Jianzhong WU, JENKINS N．, k-means based load estimation of domestic smart meter measurements[J], Applied Energy, 194, pp. 333-342, (2017)
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