Ultra-short Term Wind Power Prediction Based on Two-dimensional Coordinate Dynamic Division of Prediction Information

被引：0

作者：

Yang M. ^{[1
]}

Peng T. ^{[1
]}

Su X. ^{[1
]}

机构：

[1] Key Laboratory of Modern Power System Simulation Control and New Green Power Technology, Ministry of Education, Northeast Electric Power University, Jilin Province, Jilin

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2022年 / 42卷 / 24期

关键词：

cluster dynamic division; deep learning; power change trend; wind power prediction; wind speed fluctuation;

D O I：

10.13334/j.0258-8013.pcsee.212388

中图分类号：

学科分类号：

摘要：

The large-scale integration of wind power clusters into the power grid puts forward higher requirements for the accuracy of power prediction. In order to make full use of the predicted power information and numerical weather prediction (NWP) information, a wind farm dynamic clustering method based on two-dimensional coordinates of power change trend and wind speed change fluctuation was proposed in this paper. The 4h time scale prediction process was divided into four equal time scale cycle processes. In each 1h cycle process, the balanced iterative reduction and clustering using hierarchies (Birch) was applied to cluster the two-dimensional coordinates of each station to complete the division of clusters. According to the result of partition, the training set was constructed, the power prediction of each sub-cluster was completed by gate recurrent unit (GRU), and the process was repeated until the ultra-short-term power prediction was completed for 4 hours. The example shows that the prediction accuracy of the proposed method is improved by 1.8% compared with static partition and 4.31% compared with statistical lifting scale, which can effectively improve the power prediction accuracy of ultra-short term power prediction of wind power clusters. © 2022 Chinese Society for Electrical Engineering. All rights reserved.

引用

页码：8854 / 8863

页数：9

共 25 条

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