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

Azad, Abul Kalam and Rasul, Mohammad Golam and Yusaf, Talal (2014) Statistical diagnosis of the best weibull methods for wind power assessment for agricultural applications. Energies, 7 (5). pp. 3056-3085.

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Abstract

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 the Weibull parameters and six statistical tools, namely relative percentage of error, root mean square error (RMSE), 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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2014 by the authors. Articles labeled as 'Open Access' are licensed by its copyright holder to be further distributed or reused by the user subject to attribution and correct citation of the original source.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 11 Jun 2014 06:51
Last Modified: 04 Jul 2016 05:06
Uncontrolled Keywords: Weibull shape factor; scale factor; probability density function; power density; statistical tools
Fields of Research : 09 Engineering > 0913 Mechanical Engineering > 091305 Energy Generation, Conversion and Storage Engineering
01 Mathematical Sciences > 0104 Statistics > 010404 Probability Theory
09 Engineering > 0906 Electrical and Electronic Engineering > 090608 Renewable Power and Energy Systems Engineering (excl. Solar Cells)
Socio-Economic Objective: B Economic Development > 85 Energy > 8505 Renewable Energy > 850509 Wind Energy
Identification Number or DOI: 10.3390/en7053056
URI: http://eprints.usq.edu.au/id/eprint/25280

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