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Statistical diagnosis of the best Weibull methods for wind power assessment for agricultural applications

journal contribution
posted on 06.12.2017, 00:00 by Md Abul Kalam AzadMd Abul Kalam Azad, Mohammad RasulMohammad Rasul, T Yusaf
The best Weibull distribution methods for the assessment of wind energy potential at different altitudes in desired locations are statistically diagnosed in this study. Seven different methods, namely graphical method (GM), method of moments (MOM), standard deviation method (STDM), maximum likelihood method (MLM), power density method (PDM), modified maximum likelihood method (MMLM) and equivalent energy method (EEM) were used to estimate Weibull parameters and six statistical tools, namely relative percentage of error, root mean square error, mean percentage of error, mean absolute percentage of error, chi-square error and analysis of variance were used to precisely rank the methods. The statistical fittings of the measured and calculated wind speed data are assessed for justifying the performance of the methods. The capacity factor and total energy generated by a small model wind turbine is calculated by numerical integration using Trapezoidal sums and Simpson’s rules. The results show that MOM and MLM are the most efficient methods for determining the value of k and c to fit Weibull distribution curves.

History

Volume

7

Issue

5

Start Page

3056

End Page

3085

Number of Pages

30

ISSN

1996-1073

Location

Switzerland

Publisher

M D P I AG

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

School of Engineering and Technology (2013- ); TBA Research Institute; University of Southern Queensland;

Era Eligible

Yes

Journal

Energies.