Optimal approach for wind resource assessment using Kolmogorov–Smirnov statistic: A case study for large-scale wind farm in Pakistan

Abstract

Weibull distribution has been widely utilized for better understanding, quantification, and optimal utilization of wind energy globally. Although numerical approaches are widely used for Weibull parameters estimation (WPE), their results still lack consistency. In this situation, Artificial Intelligence Optimization Techniques (AIOT) might be a powerful tool to attaining high precision; however, in the case of WPE, standard methods may not ensure convergence. In this work, a generalized mathematical model is derived for AIOT convergence in the case of WPE. Based on the mathematical model Kolmogorov–Smirnov statistic is utilized to establish a convergence optimizer function (Copt) for WPE. The Weibull fitness tests are computed using real-time data from 13 different locations of Pakistan to check the effeteness of Copt that illustrates that Copt shows better results than numerical methods. For the economic aspect, Levelized economic cost is carried out for all the targeted sites using ten commercially available wind turbines. It is observed that five of the targeted sites are very promising for wind power production. The study is expected to provide an alternative method for WPE and the deployment of wind energy technology as a future power source in the country.