Probability distribution of wind power volatility based on the moving average method and improved nonparametric kernel density estimation

被引：0
|
作者：
Xin P. [1 ]
Liu Y. [1 ]
Yang N. [2 ,3 ]
Song X. [1 ]
Huang Y. [2 ]
机构：
[1] State Grid Economic and Technological Research Institute Co. Ltd., State Grid Office District, North District of Future Science and Technology City, North Seven Changping District, Beijing
[2] Department of Hubei Provincial Collaborative Innovation Center for New Energy Microgrid, China Three Gorges University, Yichang, 443000, Hubei Province
[3] Stevens Institute of Technology, Hoboken, 07030, NJ
来源：
Global Energy Interconnection | 2020年 / 3卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Constrained order optimization; Kernel density estimation; Moving average method; Signal decomposition; Wind power fluctuation characteristics;
D O I：
10.1016/j.gloei.2020.07.006
中图分类号：
学科分类号：
摘要：
In the process of large-scale, grid-connected wind power operations, it is important to establish an accurate probability distribution model for wind farm fluctuations. In this study, a wind power fluctuation modeling method is proposed based on the method of moving average and adaptive nonparametric kernel density estimation (NPKDE) method. Firstly, the method of moving average is used to reduce the fluctuation of the sampling wind power component, and the probability characteristics of the modeling are then determined based on the NPKDE. Secondly, the model is improved adaptively, and is then solved by using constraint-order optimization. The simulation results show that this method has a better accuracy and applicability compared with the modeling method based on traditional parameter estimation, and solves the local adaptation problem of traditional NPKDE. © 2020
引用
收藏
页码：247 / 258
页数：11
相关论文
共 50 条
  • [1] Probability distribution of wind power volatility based on the moving average method and improved nonparametric kernel density estimation
    Peizhe Xin
    Ying Liu
    Nan Yang
    Xuankun Song
    Yu Huang
    Global Energy Interconnection, 2020, 3 (03) : 247 - 258
  • [2] Study on Probability Distribution of Wind Power Fluctuation Based on NACEMD and Improved Nonparametric Kernel Density Estimation
    Yang N.
    Huang Y.
    Ye D.
    Yan J.
    Zhang L.
    Dong B.
    Dianwang Jishu/Power System Technology, 2019, 43 (03): : 910 - 917
  • [3] Research on Probability Density Modeling Method of Wind Power Fluctuation Based on Nonparametric Kernel Density Estimation
    Chen, Daojun
    Guo, Hu
    Zuo, Jian
    Cui, Ting
    Shen, Yangwu
    Zhang, Lei
    2018 3RD INTERNATIONAL CONFERENCE ON INTELLIGENT GREEN BUILDING AND SMART GRID (IGBSG 2018), 2018,
  • [4] Research on Modeling Method Describing Wind Power Fluctuation Probability Density Based on Nonparametric Kernel Density Estimation
    Chen, Daojun
    Guo, Hu
    Zuo, Jian
    Zhang, Lei
    Wang, Xuan
    2016 3RD INTERNATIONAL CONFERENCE ON SYSTEMS AND INFORMATICS (ICSAI), 2016, : 178 - 187
  • [5] Research on nonparametric kernel density estimation for modeling of wind power probability characteristics based on fuzzy ordinal optimization
    Yang N.
    Cui J.
    Zhou Z.
    Zhang S.
    Zhang L.
    Liu D.
    Hu W.
    1600, Power System Technology Press (40): : 335 - 340
  • [6] Wind Power Prediction Based on LSTM Networks and Nonparametric Kernel Density Estimation
    Zhou, Bowen
    Ma, Xiangjin
    Luo, Yanhong
    Yang, Dongsheng
    IEEE ACCESS, 2019, 7 : 165279 - 165292
  • [7] Probability Density Estimation Based on Nonparametric Local Kernel Regression
    Han, Min
    Liang, Zhi-ping
    ADVANCES IN NEURAL NETWORKS - ISNN 2010, PT 1, PROCEEDINGS, 2010, 6063 : 465 - 472
  • [8] Probability prediction of wind power based on QR-NFGLSTM and kernel density estimation
    Wang, Xiaodong
    Ju, Bangguo
    Liu, Yingming
    Zang, Tonglin
    Taiyangneng Xuebao/Acta Energiae Solaris Sinica, 2022, 43 (02): : 479 - 485
  • [9] Research of modeling method based on non-parametric kernel density estimation of probability of wind power fluctuations
    Yang, Nan
    Zhou, Zheng
    Chen, Daojun
    Wang, Xuan
    Li, Hongsheng
    Li, Suoya
    Taiyangneng Xuebao/Acta Energiae Solaris Sinica, 2019, 40 (07): : 2028 - 2035
  • [10] A mixture kernel density model for wind speed probability distribution estimation
    Miao, Shuwei
    Xie, Kaigui
    Yang, Hejun
    Karki, Rajesh
    Tai, Heng-Ming
    Chen, Tao
    ENERGY CONVERSION AND MANAGEMENT, 2016, 126 : 1066 - 1083
12345