Weibull model for wind speed data analysis of different locations in India

被引:0
|
作者
Arnab Sarkar
Gaurav Gugliani
Sneh Deep
机构
[1] Indian Institute of Technology (Banaras Hindu University),Dept. of Mechanical Engineering
[2] Indian Institute of Science,Department of Aerospace Engineering
来源
关键词
wind speed; Weibull distribution; sampling error; modified maximum likelihood method; fatigue failure; wind energy;
D O I
暂无
中图分类号
学科分类号
摘要
Wind speed data should be fitted by a suitable statistical model like Weibull to determine expected number of hours per year in the critical wind speed range for a slender structure, which is required to determine the expected number of stress cycles in the projected working life of the structure. Apart from this, for the assessment of wind energy potential wind speed data should be fitted by an appropriate probability distribution. In the present scope of study, wind data of various locations of India have been fitted by Weibull model. Wind speed data are initially sampled in knot by Indian Meteorological Department and later converted into integer km/h before supplying them to the end user. Due to this conversion, wind speed data cannot be properly fitted by Weibull distribution and in this regard, the choice of appropriate class width becomes very much important. Without the choice of appropriate class width, estimated Weibull parameters become biased which would yield incorrect estimation of expected number of hours in critical wind speed ranges as well as wind energy potential. After taking appropriate class width of 4 km/h, it has been found that Weibull model is an adequate model to describe wind speed distributions of India. Weibull model has also been compared with other models such as Gamma and inverse Weibull distributions to establish its suitability than the others. In this study, the values of Weibull shape parameters vary from 1.3 to 2.3, whereas the values of scale parameters vary from 1.4 m/s to 6.5 m/s. The validity of Weibull model is also verified with a target confidence interval of 90%. The uncertainties involved in the estimation of available wind energy potential as well as the expected number of hours per year in critical wind speed ranges have also been considered due to random variation of wind climate in each year.
引用
收藏
页码:2764 / 2776
页数:12
相关论文
共 50 条
  • [1] Weibull model for wind speed data analysis of different locations in India
    Sarkar, Arnab
    Gugliani, Gaurav
    Deep, Sneh
    [J]. KSCE JOURNAL OF CIVIL ENGINEERING, 2017, 21 (07) : 2764 - 2776
  • [2] Weibull and Generalized Extreme Value Distributions for Wind Speed Data Analysis of Some Locations in India
    Arnab Sarkar
    Sneh Deep
    D. Datta
    Amit Vijaywargiya
    R. Roy
    V. S. Phanikanth
    [J]. KSCE Journal of Civil Engineering, 2019, 23 : 3476 - 3492
  • [3] Weibull and Generalized Extreme Value Distributions for Wind Speed Data Analysis of Some Locations in India
    Sarkar, Arnab
    Deep, Sneh
    Datta, D.
    Vijaywargiya, Amit
    Roy, R.
    Phanikanth, V. S.
    [J]. KSCE JOURNAL OF CIVIL ENGINEERING, 2019, 23 (08) : 3476 - 3492
  • [4] Analysis of Wind Speed Data for Four Locations in Ireland based on Weibull Distribution's Linear Regression Model
    Jamdade, Shrinivas Gautamrao
    Jamdade, Parikshit Gautam
    [J]. INTERNATIONAL JOURNAL OF RENEWABLE ENERGY RESEARCH, 2012, 2 (03): : 451 - 455
  • [6] Estimation of the wind energy potential for coastal locations in India using the Weibull model
    Deep, Sneh
    Sarkar, Arnab
    Ghawat, Mayur
    Rajak, Manoj Kumar
    [J]. RENEWABLE ENERGY, 2020, 161 : 319 - 339
  • [7] A Finite Mixture Three-Parameter Weibull Model for the Analysis of Wind Speed Data
    Qin, Xu
    Zhang, Jiang-She
    Yan, Xiao-Dong
    [J]. COMMUNICATIONS IN STATISTICS-THEORY AND METHODS, 2012, 41 (12) : 2160 - 2171
  • [8] Comparative Analysis of Wind Speed Models Using Different Weibull Distributions
    Dokur, Emrah
    Ceyhan, Salim
    Kurban, Mehmet
    [J]. ELECTRICA, 2019, 19 (01): : 22 - 28
  • [9] Statistical Analysis of Wind Speed Data Using Weibull Distribution Parameters
    Chauhan, Anurag
    Saini, R. P.
    [J]. PROCEEDINGS OF 2014 1ST INTERNATIONAL CONFERENCE ON NON CONVENTIONAL ENERGY (ICONCE 2014), 2014, : 160 - 163
  • [10] Two Improved Mixture Weibull Models for the Analysis of Wind Speed Data
    Qin, Xu
    Zhang, Jiang-she
    Yan, Xiao-dong
    [J]. JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY, 2012, 51 (07) : 1321 - 1332