Comparative study of numerical methods for determining Weibull parameters for wind energy potential

Weibull distribution has been one of the most widely used distribution to determine potential of wind energy. Many different numerical methods can be used to estimate the parameters of the Weibull distribution. The L-moment method (L-MoM), which has not been used extensively in the previous literature about wind energy for the estimation of wind speed parameters relevant to the Weibull distribution has been presented and this method has been compared to the Moment method (MoM) and Maximum Likelihood (ML) method. Monte Carlo simulation has been used to compare the methods used in the estimation of the shape (k) and scale (c) parameters for a Weibull distribution. Moreover, MoM, L-MoM and ML parameter estimation methods have been used in analyzing an actual data set. Wind power densities have also been calculated with the help of estimated parameter values. We showed that, distribution is skewed to the right or is symmetrical and n≥100 the ML method is preferable in comparison to other methods in the estimation of the shape (k) parameter. The L-MoM method which we presented in this study may be beneficial for research using small sample sizes.

[1]  Nasrudin Abd Rahim,et al.  Assessment of wind energy potentiality at Kudat and Labuan, Malaysia using Weibull distribution function , 2011 .

[2]  A. Hepbasli,et al.  A review on the development of wind energy in Turkey , 2004 .

[3]  Tarkan Erdik,et al.  Theoretical derivation of wind power probability distribution function and applications , 2012 .

[4]  T. W. Lambert,et al.  Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis , 2000 .

[5]  Henrik Lund,et al.  Renewable energy strategies for sustainable development , 2007 .

[6]  H. S. Bagiorgas,et al.  Use of two-component Weibull mixtures in the analysis of wind speed in the Eastern Mediterranean , 2010 .

[7]  Nicholas J. Cook,et al.  “Discussion on modern estimation of the parameters of the Weibull wind speed distribution for wind speed energy analysis” by J.V. Seguro, T.W. Lambert , 2001 .

[8]  J. A. Carta,et al.  Use of finite mixture distribution models in the analysis of wind energy in the Canarian Archipelago , 2007 .

[9]  A. Hepbasli,et al.  Determination of Weibull parameters for wind energy analysis of İzmir, Turkey , 2002 .

[10]  J. Hosking L‐Moments: Analysis and Estimation of Distributions Using Linear Combinations of Order Statistics , 1990 .

[11]  Mehmet Bilgili,et al.  Statistical Analysis of Wind Energy Density in the Western Region of Turkey , 2010 .

[12]  T. Chang Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application , 2011 .

[13]  U. Bardi Peak oil: The four stages of a new idea , 2009 .

[14]  J. A. Carta,et al.  Influence of the data sampling interval in the estimation of the parameters of the Weibull wind speed probability density distribution: a case study , 2005 .

[15]  S. M. Hoseini,et al.  Comparison of estimation methods for the Weibull distribution , 2013 .

[16]  J. A. Carta,et al.  Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions , 2007 .

[17]  J. A. Carta,et al.  A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands , 2009 .

[18]  Timothy M. Whalen,et al.  An evaluation of the self-determined probability-weighted moment method for estimating extreme wind speeds , 2004 .

[19]  Hee-Chang Lim,et al.  Wind energy estimation of the Wol-Ryong coastal region , 2010 .

[20]  Ahmed S. Ahmed,et al.  Wind energy as a potential generation source at Ras Benas, Egypt , 2010 .

[21]  O. A. Jaramillo,et al.  Wind speed analysis in La Ventosa, Mexico: a bimodal probability distribution case , 2004 .

[22]  Seyit Ahmet Akdağ,et al.  A new method to estimate Weibull parameters for wind energy applications , 2009 .

[23]  Mehmet Bilgili,et al.  Wind Characteristics and Energy Potential in Belen-Hatay, Turkey , 2009 .

[24]  Bora Alboyaci,et al.  An Evaluation of Wind Energy Characteristics for Four Different Locations in Balikesir , 2011 .

[25]  Chandrabhan Sharma,et al.  Wind Speed Distributions: A New Catalogue of Defined Models , 2001 .

[26]  D. Weisser,et al.  A wind energy analysis of Grenada: an estimation using the 'Weibull' density function , 2003 .