SHORT-TERM PREDICTION OF WIND POWER CONSIDERING LOCAL CONDITION FEATURES

被引：0

作者：

Zhang, Jiaan ^{[1
,2
]}

Huang, Chenxu ^{[2
]}

Li, Zhijun ^{[1
,2
]}

机构：

[1] State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin

[2] School of Electrical Engineering, Hebei University of Technology, Tianjin

来源：

Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2024年 / 45卷 / 12期

关键词：

cluster analysis; feature extraction; forecasting; neural networks; wind power;

D O I：

10.19912/j.0254-0096.tynxb.2023-1183

中图分类号：

学科分类号：

摘要：

A short-term prediction method of wind power considering local condition features is proposed. Firstly，based on the Spearman correlation coefficient，the correlation between local condition factors and wind turbine power is analyzed. Wind speed，wind direction together with yaw angle are selected as key factors. Then，the distribution parameters of key factors are estimated separately with the generalized extreme value distribution，and an average fluctuation coefficient index is constructed to describe the parameter differences between each wind turbine. The wind turbines are clustered into several groups with the K-means++ algorithm. Finally，the key features of each wind turbine cluster are extracted with principal component analysis（PCA）. Based on Bidirectional gated recurrent units（BiGRU），the power of the cluster is accurately predicted and accumulated. Taking the operation data of a wind farm in North China as an example，the effectiveness of this method is verified. © 2024 Science Press. All rights reserved.

引用

页码：220 / 227

页数：7

共 23 条

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