Improved wind prediction based on the Lorenz system

被引:17
|
作者
Zhang, Yagang [1 ,2 ]
Yang, Jingyun [1 ]
Wang, Kangcheng [1 ]
Wang, Zengping [1 ]
Wang, Yinding [2 ]
机构
[1] North China Elect Power Univ, State Key Lab Alternate Elect Power Syst Renewabl, Being 102206, Peoples R China
[2] Univ S Carolina, Interdisciplinary Math Inst, Columbia, SC 29208 USA
关键词
Lorenz system; Atmospheric disturbance; Wavelet neural network; Disturbance coefficient; Disturbance intensity; RENEWABLE ENERGY; SPEED; OPTIMIZATION; FARM;
D O I
10.1016/j.renene.2015.03.039
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Atmospheric disturbance is a complex nonlinear process. The Lorenz system was seen as a classical model to reveal essential characteristics of nonlinear systems. It has further improved people's understanding of the evolution of the climate system. Different from traditional studies working on improving the numerical methods for wind prediction, dynamic characteristics of the atmospheric system are fully considered here. This paper proposed the concept of the Lorenz Comprehensive Disturbance Flow (LCDF) and defined the perturbation formula for wind prediction. The Lorenz disturbance has significant influence on wind forecasting, which is proved by using wind data from the Sotavento wind farm. That is to say, the change process of atmospheric motion around the wind farm is more ideally described based on the Lorenz system. This research has important theoretical value in developing nonlinear systems and plays a great role on wind prediction and wind resource exploitation. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:219 / 226
页数:8
相关论文
共 50 条
  • [31] An Improved IOT based Standalone Hybrid(Pv/Wind) System
    Nayak, Deepak Kumar
    Prabakaran, N.
    Murugan, R.
    Albert, Anitha Juliette
    2018 INTERNATIONAL CONFERENCE ON RECENT INNOVATIONS IN ELECTRICAL, ELECTRONICS & COMMUNICATION ENGINEERING (ICRIEECE 2018), 2018, : 2018 - 2020
  • [32] Lorenz Wind Disturbance Model Based on Grey Generated Components
    Zhang, Yagang
    Yang, Jingyun
    Wang, Kangcheng
    Wang, Yinding
    ENERGIES, 2014, 7 (11): : 7178 - 7193
  • [33] Prediction of short-term wind power based on ESN improved by VMD
    Gao Xu
    Tang Zhenhao
    Han Hongzhi
    Bu Bing
    PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021), 2021, : 674 - 678
  • [34] Real-Time Prediction of the Wind Power Based on Improved Sustainable Model
    Yang, Mao
    Jia, Yunpeng
    PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS ENGINEERING (CASE-13), 2013, 45 : 115 - 119
  • [35] Interval prediction method of wind power based on improved chaotic time series
    Li J.
    Huang Y.
    Huang Q.
    Dianli Zidonghua Shebei/Electric Power Automation Equipment, 2019, 39 (05): : 53 - 60and68
  • [36] Improved EMD-Based Complex Prediction Model for Wind Power Forecasting
    Abedinia, Oveis
    Lotfi, Mohamed
    Bagheri, Mehdi
    Sobhani, Behrouz
    Shafie-khah, Miadreza
    Catalao, Joao P. S.
    IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2020, 11 (04) : 2790 - 2802
  • [37] IMPROVED WEIGHTED GRU WIND POWER INTERVAL PREDICTION BASED ON QUANTILE REGRESSION
    Liu, Tianhong
    Qi, Shengli
    Yi, Yang
    Jian, Libin
    Qiao, Xianzhu
    Zhang, Enze
    Taiyangneng Xuebao/Acta Energiae Solaris Sinica, 2024, 45 (12): : 292 - 298
  • [38] Wind Power Interval Prediction Based on Improved PSO and BP Neural Network
    Wang, Jidong
    Fang, Kaijie
    Pang, Wenjie
    Sun, Jiawen
    JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY, 2017, 12 (03) : 989 - 995
  • [39] Fault prediction of wind turbine gearbox based on improved hidden Markov model
    Zeng, Chengzhi (2854980@qq.com), 1600, Science Press (37):
  • [40] Economic Analysis of System Spinning Reserve Based on Improved CNN-LSTM Short Term Wind Power Prediction
    Chen H.
    Zhou Y.
    Wang C.
    Wang J.
    Han H.
    Lü X.
    Gaodianya Jishu/High Voltage Engineering, 2022, 48 (02): : 439 - 446