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Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications

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  • Abul Kalam Azad

    (School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4702, Australia)

  • Mohammad Golam Rasul

    (School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4702, Australia)

  • Talal Yusaf

    (National Centre for Engineering in Agriculture, Faculty of Engineering and Surveying, University of Southern Queensland, Toowoomba, QLD 4350, Australia)

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.

Suggested Citation

  • Abul Kalam Azad & Mohammad Golam Rasul & Talal Yusaf, 2014. "Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications," Energies, MDPI, vol. 7(5), pages 1-30, May.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:5:p:3056-3085:d:35706
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