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,300401, China
[2] School of Electrical Engineering, Hebei University of Technology, Tianjin,300401, China
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2024年 / 45卷 / 12期
关键词：
Cluster analysis - Principal component analysis - Wind forecasting - Wind power - Wind turbines;
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
收藏
页码：220 / 227
相关论文
共 50 条
  • [1] Short-term Prediction of Wind Power Considering Turbulence Intensity
    Huang R.
    Du W.
    Wang H.
    Dianwang Jishu/Power System Technology, 2019, 43 (06): : 1907 - 1913
  • [2] Short-Term Prediction of Wind Power Considering the Fusion of Multiple Spatial and Temporal Correlation Features
    Wu, Fangze
    Yang, Mao
    Shi, Chaoyu
    FRONTIERS IN ENERGY RESEARCH, 2022, 10
  • [3] Combined Prediction of Short-term Wind Power Considering Correlation of Meteorological Features and Fluctuation Process
    Ye L.
    Zhao J.
    Lu P.
    Pei M.
    Chen M.
    Wang B.
    Che J.
    Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2021, 45 (04): : 54 - 62
  • [4] Short-Term Prediction of Wind Power Considering the Fusion of Multiple Spatial and Temporal Correlation Features
    Wu, Fangze
    Yang, Mao
    Shi, Chaoyu
    Frontiers in Energy Research, 2022, 10
  • [5] Short-term Prediction of Offshore Wind Power Considering Wind Direction and Atmospheric Stability
    Qi C.
    Wang X.
    Dianwang Jishu/Power System Technology, 2021, 45 (07): : 2773 - 2780
  • [6] SHORT-TERM PREDICTION OF LOCAL WIND CONDITIONS
    LANDBERG, L
    WATSON, SJ
    BOUNDARY-LAYER METEOROLOGY, 1994, 70 (1-2) : 171 - 195
  • [7] Short-term prediction of local wind conditions
    Landberg, L
    WIND ENGINEERING INTO THE 21ST CENTURY, VOLS 1-3, 1999, : 1997 - 2003
  • [8] Short-term Wind Power Probabilistic Prediction Considering Data and Model Uncertainties
    Yu J.
    Pang C.
    Dianwang Jishu/Power System Technology, 2022, 46 (05): : 1926 - 1933
  • [9] Short-term prediction of local wind conditions
    Landberg, L
    JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS, 2001, 89 (3-4) : 235 - 245
  • [10] The ultra-short term power prediction of wind farm considering operational condition of wind turbines
    Fang, Ruiming
    Wang, Yandong
    Shang, Rongyan
    Liang, Yin
    Wang, Li
    Peng, Chongqing
    INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2016, 41 (35) : 15733 - 15739
12345