Abstract
The integration of wind power as an alternative energy source has gotten much attention globally. In this paper, the Weibull distribution model based on different artificial intelligent algorithms and numerical methods is used to evaluate the wind profile. The application of Weibull distribution in wind data assessment can be extensively found, but the methods applied for estimating the parameters still need improvement. Three artificial intelligent algorithms are presented as an alternative method for estimation of Weibull parameters, and an objective function is proposed through the concept of maximum distance metric. Its convergence was proven mathematically through its boundedness for all wind data types. The optimization methods based on the proposed objective function are compared with the conventional numerical approaches for Weibull parameter estimation. Two-year wind data from the site in the southern area of Pakistan has been used to conduct this analysis. Furthermore, this work provides an eloquent way for the selection of a suitable area, evaluation of parameters, and appropriate wind turbine models through real-time data for power production.
Highlights
The renewable energy sector has been encouraged to develop due to its endless resource, lower index of greenhouse gas emissions, and better economics
Turbulence intensity, extreme wind speed, wind directions, and annual energy production are examined to give an optimal assessment of the respective area
The precise estimation of its parameters remains a challenge for wind community researchers and developers
Summary
The renewable energy sector has been encouraged to develop due to its endless resource, lower index of greenhouse gas emissions, and better economics. Wind resource analyses are performed to predict the performance and output of potential power projects. This allows investors and developers to gather more accurate feasibility of the project under consideration. Luna and Church [9] determined that the application of probability distribution on wind data depends more on the considered data. According to their analysis, lognormal distribution was the best representation of wind data. Several distribution functions have been utilized for wind data representation and can be found in the literature extensively [10,11,12,13]
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