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
  • [21] Ultra-short-term wind power forecasting method based on a cross LOF preprocessing algorithm and an attention mechanism
    Wang X.
    Cai X.
    Li Z.
    Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control, 2020, 48 (23): : 92 - 99
  • [22] An integrated ultra short term power forecasting method for regional wind-pv-hydro
    Dong, Lizhi
    Li, Yuyang
    Xiu, Xiaoqing
    Li, Zhicheng
    Zhang, Weijun
    Chen, Dawei
    ENERGY REPORTS, 2023, 9 : 1531 - 1540
  • [23] An integrated ultra short term power forecasting method for regional wind-pv-hydro
    Dong, Lizhi
    Li, Yuyang
    Xiu, Xiaoqing
    Li, Zhicheng
    Zhang, Weijun
    Chen, Dawei
    ENERGY REPORTS, 2023, 9 : 1531 - 1540
  • [24] A review on short-term and ultra-short-term wind power prediction
    Xue, Yusheng
    Yu, Chen
    Zhao, Junhua
    Li, Kang
    Liu, Xueqin
    Wu, Qiuwei
    Yang, Guangya
    Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2015, 39 (06): : 141 - 151
  • [25] DTTM: A deep temporal transfer model for ultra-short-term online wind power forecasting
    Zhong, Mingwei
    Xu, Cancheng
    Xian, Zikang
    He, Guanglin
    Zhai, Yanpeng
    Zhou, Yongwang
    Fan, Jingmin
    ENERGY, 2024, 286
  • [26] Forecasting ultra-short-term wind power by multiview gated recurrent unit neural network
    Xiong, Bangru
    Fu, Mengqin
    Cai, Qiuting
    Li, Xiaoyan
    Lou, Lu
    Ma, Hui
    Meng, Xinyu
    Wang, Zhengxia
    ENERGY SCIENCE & ENGINEERING, 2022, 10 (10) : 3972 - 3986
  • [27] A novel model based on CEEMDAN, IWOA, and LSTM for ultra-short-term wind power forecasting
    Shaomei Yang
    Aijia Yuan
    Zhengqin Yu
    Environmental Science and Pollution Research, 2023, 30 (5) : 11689 - 11705
  • [28] Modes decomposition forecasting approach for ultra-short-term wind speed
    Tian, Zhongda
    APPLIED SOFT COMPUTING, 2021, 105
  • [29] Research on Ultra-Short-Term Wind Power Forecasting Based on Refactored Representation of Environmental Features
    Wang, Feng
    Jiang, Jiading
    Zhang, Lingling
    PROCEEDINGS OF 2021 IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY WORKSHOPS AND SPECIAL SESSIONS: (WI-IAT WORKSHOP/SPECIAL SESSION 2021), 2021, : 375 - 379
  • [30] Ultra-short-term wind power forecasting based on contrastive learning-assisted training
    Wang Y.
    Zhu N.
    Xie H.
    Li J.
    Zhang K.
    Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument, 2023, 44 (03): : 89 - 97