Comparison of Weibull parameters computation methods and analytical estimation of wind turbine capacity factor using polynomial power curve model: case study of a wind farm

Introduction: Wind speed probability at a site has to be modeled for evaluating the energy generation potential of a wind farm. Analytical computation of wind turbine capacity factor at the planning stage of a wind farm is very crucial. Thus, the comparison of Weibull parameters estimation methods and computation of wind turbine capacity factor are the focus of this paper. Case description: Soda wind farm used in this case study is located in the Jaisalmer district of western Rajasthan in India. Modeling of wind speed probability and power curve of wind turbines installed at Soda site were done for analytically estimating the capacity factor of wind turbine. Estimated capacity factors were then compared with the measured values of wind farm for validation of results. Discussion and evaluation: Four numerical methods namely graphical, empirical, modified maximum likelihood, and energy pattern factor were used for month-wise Weibull parameters estimation at hub height of 65 m. Power curve of the wind turbine installed at site was modeled using eighth-degree polynomial. Coefficients of polynomial were calculated from the combined use of linear least square method and QR decomposition using Gram-Schmidt orthogonalization method. Conclusions: Results show that the percentage error in annual capacity factor estimation using Weibull parameters estimated from graphical, empirical, modified maximum likelihood, and energy pattern factor methods were +9.98%, −1.59%, −1.22%, and −1.29%, respectively. Annual capacity factor that was estimated using the Weibull parameters calculated from modified maximum likelihood method matched best with the measured values. Graphical method gave the most erroneous results.