Fractional Weibull Wind Speed Modeling For Wind Power Production Estimation

被引:0
|
作者
Yu, Zuwei
Tuzuner, Akiner
机构
关键词
fitting error; fractional Weibull; Mean-Variance; MLE; uniqueness; wind speed; wind power; GENERATION; SYSTEM;
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
This paper describes the method of the Fractional Weibull Distribution (FWD) for modeling wind speed distributions. The Maximum Likelihood Estimation (MLE) method is used for estimating the parameters of the FWD, fitted to the wind data from a tall tower. Seasonal wind speed variations are considered in the modeling. Compared to the standard Weibull distribution estimates, the FWD estimates yield greater accuracy in wind power estimation. The discrete distributions of the probabilities of the FWDs are used for obtaining expected wind energy production, which shows a considerable reduction in errors by about tenfold. A simple Mean-Variance analysis of power production is performed for a wind farm that can have a mix of three different turbine models. The results indicate that the standard deviation of power production can be considerably reduced by choosing an appropriate mix of turbines.
引用
收藏
页码:2874 / 2880
页数:7
相关论文
共 50 条
  • [21] Analysis of the influence of the wind speed profile on wind power production
    Lopez-Villalobos, C. A.
    Martinez-Alvarado, O.
    Rodriguez-Hernandez, O.
    Romero-Centeno, R.
    [J]. ENERGY REPORTS, 2022, 8 : 8079 - 8092
  • [22] A Review on Wind Speed Extreme Values Modeling and Bayes Estimation for Wind Power Plant Design and Construction
    Chiodo, Elio
    Diban, Bassel
    Mazzanti, Giovanni
    De Angelis, Fabio
    [J]. ENERGIES, 2023, 16 (14)
  • [23] Deep generative model for probabilistic wind speed and wind power estimation at a wind farm
    Salazar, Andres A.
    Zheng, Jiafeng
    Che, Yuzhang
    Xiao, Feng
    [J]. ENERGY SCIENCE & ENGINEERING, 2022, 10 (06) : 1855 - 1873
  • [24] Statistical analysis of wind speed and wind power potential of Port Elizabeth using Weibull parameters
    Ayodele, Temitope R.
    Jimoh, Adisa A.
    Munda, Josiah L.
    Agee, John T.
    [J]. JOURNAL OF ENERGY IN SOUTHERN AFRICA, 2012, 23 (02) : 30 - 38
  • [25] Stochastic wind speed modelling for estimation of expected wind power output
    Loukatou, Angeliki
    Howell, Sydney
    Johnson, Paul
    Duck, Peter
    [J]. APPLIED ENERGY, 2018, 228 : 1328 - 1340
  • [26] Wind resource estimation using wind speed and power curve models
    Lydia, M.
    Kumar, S. Suresh
    Selvakumar, A. Immanuel
    Kumar, G. Edwin Prem
    [J]. RENEWABLE ENERGY, 2015, 83 : 425 - 434
  • [27] Wind farm production estimation under multivariate wind speed distribution
    Chiodo, E.
    Lauria, D.
    Pisani, C.
    [J]. 2013 4TH INTERNATIONAL CONFERENCE ON CLEAN ELECTRICAL POWER (ICCEP): RENEWABLE ENERGY RESOURCES IMPACT, 2013, : 745 - 750
  • [28] Modeling the effect of wind speed and direction shear on utility-scale wind turbine power production
    Mata, Storm A.
    Martinez, Juan Jose Pena
    Quesada, Jesus Bas
    Larranaga, Felipe Palou
    Yadav, Neeraj
    Chawla, Jasvipul S.
    Sivaram, Varun
    Howland, Michael F.
    [J]. WIND ENERGY, 2024, 27 (09) : 873 - 899
  • [29] Bayes Estimation of Inverse Weibull Distribution for Extreme Wind Speed Prediction
    Chiodo, E.
    Mazzanti, G.
    Karimian, M.
    [J]. 2015 INTERNATIONAL CONFERENCE ON CLEAN ELECTRICAL POWER (ICCEP), 2015, : 639 - 646
  • [30] Estimation of monthly wind speed distribution basing on hybrid Weibull distribution
    Ihaddadene, Razika
    Ihaddadene, Nabila
    Mostefaoui, Marouane
    [J]. WORLD JOURNAL OF ENGINEERING, 2016, 13 (06) : 509 - 515