An ultra-short-term wind power forecasting method in regional grids

被引:0
|
作者
Li, Zhi [1 ]
Han, Xueshan [1 ]
Han, Li [2 ]
Kang, Kai [3 ]
机构
[1] Shandong University, Jinan 250061, China
[2] China International Engineering Consulting Corporation, Beijing 100044, China
[3] Yantai Power Supply Company, Yantai 264001, China
关键词
Wind farm - Electric power transmission networks - Bandpass filters - Weather forecasting - Electric power system interconnection - Electric utilities;
D O I
暂无
中图分类号
学科分类号
摘要
Considering a regional grid with several wind farms integrated, the total wind power has a better regularity comparing to that of a single wind farm. An ultra-short-term wind power forecasting method is proposed based on the concepts of total wind power and distribution factor. The least-square support vector machine (LS-SVM) and Kalman filter are adopted respectively to forecast the total wind power and distribution factor recursively, so that the good regularity of total wind power can be restored. Case studies show that the method not only improves the forecasting accuracy but also reduces the distribution range of the forecasting errors. © 2010 State Grid Electric Power Research Institute Press.
引用
收藏
页码:90 / 94
相关论文
共 50 条
  • [31] Ultra-short-term wind power forecasting based on TCN-Wpsformer hybrid model
    Xu, Tan
    Xie, Kaigui
    Wang, Yu
    Hu, Bo
    Shao, Changzheng
    Zhao, Yusheng
    Dianli Zidonghua Shebei/Electric Power Automation Equipment, 2024, 44 (08): : 54 - 61
  • [32] Hedge Backpropagation Based Online LSTM Architecture for Ultra-Short-Term Wind Power Forecasting
    Pan, Chunyang
    Wen, Shuli
    Zhu, Miao
    Ye, Huili
    Ma, Jianjun
    Jiang, Sheng
    IEEE TRANSACTIONS ON POWER SYSTEMS, 2024, 39 (02) : 4179 - 4192
  • [33] A Hybrid GA-PSO-CNN Model for Ultra-Short-Term Wind Power Forecasting
    Liu, Jie
    Shi, Quan
    Han, Ruilian
    Yang, Juan
    ENERGIES, 2021, 14 (20)
  • [34] Research on Improvement of Ultra-short-term Wind Power Forecasting Model Based on Chaos Theory
    Yang M.
    Sun Z.
    Su X.
    Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 2022, 42 (22): : 8117 - 8128
  • [35] Ultra-short-term forecasting of wind power based on multi-task learning and LSTM
    Junqiang, Wei
    Xuejie, Wu
    Tianming, Yang
    Runhai, Jiao
    INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2023, 149
  • [36] A Power Forecasting Method for Ultra-Short-Term Photovoltaic Power Generation Using Transformer Model
    Tian, Fengyuan
    Fan, Xuexin
    Wang, Ruitian
    Qin, Haochen
    Fan, Yaxiang
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2022, 2022
  • [37] Ultra-short-term wind power multi-step forecasting based on improved AWNN
    Lu J.
    Zeng Y.
    Yu H.
    Liang P.
    Zhuang Y.
    Ge J.
    Taiyangneng Xuebao/Acta Energiae Solaris Sinica, 2021, 42 (01): : 166 - 173
  • [38] Ultra-Short-Term Wind Power Forecasting in Complex Terrain: A Physics-Based Approach
    Michos, Dimitrios
    Catthoor, Francky
    Foussekis, Dimitris
    Kazantzidis, Andreas
    ENERGIES, 2024, 17 (21)
  • [39] A novel model based on CEEMDAN, IWOA, and LSTM for ultra-short-term wind power forecasting
    Yang, Shaomei
    Yuan, Aijia
    Yu, Zhengqin
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2023, 30 (05) : 11689 - 11705
  • [40] IDHNet: Ultra-Short-Term Wind Power Forecasting With IVMD-DCInformer-HSSA Network
    Li, Wei
    Gao, Lu
    Zhang, Fei
    Ren, XiaoYing
    Qin, Ling
    ENERGY SCIENCE & ENGINEERING, 2024,