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.
引用
收藏
页码：4607 / 4618
页数：11
相关论文
共 34 条
  • [1] ZHU Xiaoxun, HAN Zhonghe, Research on LS-SVM wind speed prediction method based on PSO[J], Proceedings of the CSEE, 36, 23, pp. 6337-6342, (2016)
  • [2] CHEN Chenpeng, ZHAO Xin, BI Guihong, SSA-Res-GRU short-term wind speed prediction model based on multi-model decomposition[J], Power System Technology, 46, 8, pp. 2975-2985, (2022)
  • [3] HAN Li, LI Mengjie, QIAO Yan, Ultra-short-term prediction of wind power based on low-frequency fluctuation mining and high-frequency correction[J], Power System Technology, 46, 7, pp. 2750-2758, (2022)
  • [4] DAI Jianfeng, YAN Cheng, TANG Yi, Ultra-short-term wind speed hybrid forecasting model for wind farms based on time series residual probability modeling[J], Power System Technology, 47, 2, pp. 688-697, (2023)
  • [5] CHEN Haipeng, LI He, KAN Tianyang, DWT-DTCNA ultra-short-term wind power prediction considering wind power timing characteristics[J], Power System Technology, 47, 4, pp. 1653-1662, (2023)
  • [6] MIAO Changxin, WANG Xia, LI Hao, Day-ahead prediction of wind power based on NWP wind speed error correction[J], Power System Technology, 46, 9, pp. 3455-3462, (2022)
  • [7] MU Gang, YANG Xiuyu, YAN Gangui, Prediction method of the durative characteristic for wind farm cluster based on cumulative evolution tendency[J], Proceedings of the CSEE, 38, S1, pp. 32-38, (2018)
  • [8] HUANG Lingling, LI Suo, FU Yang, Ultra-short term offshore wind power prediction based on condition-assessment of wind turbines[J], Acta Energiae Solaris Sinica, 43, 8, pp. 391-398, (2022)
  • [9] Chen WANG, KOU Peng, WANG Ruogu, Wind speed forecasting for multiple wind turbines with point cloud distribution using spatio-temporal correlation on multiple spatial scale[J], Automation of Electric Power Systems, 45, 22, pp. 65-73, (2021)
  • [10] CHEN Jinfu, ZHU Qiaomu, SHI Dongyuan, A multi-step wind speed prediction model for multiple sites leveraging spatio-temporal correlation[J], Proceedings of the CSEE, 39, 7, pp. 2093-2105, (2019)
1234