Expanding the Yearly Profit of Wind Farm Using Genetic Algorithm with Variable Allocation Method of Possibilities for Crossover and Mutation Procedures

Abstract

With rising surface air temperature, global communities are continually struggling to restrict the production of greenhouse gases through the competent application of renewable resources. Being a proficient alternative to traditionalelectricity generation technologies, wind energy can facilitate nations to achieve their carbon neutrality goals. This paper aims to enhance the annual profit of wind farms using an enriched genetic algorithm. Innovative dynamic techniques for allotting the chances of crossover and mutation procedures have been employed for the genetic algorithm-based optimization process accompanied by the established static tactic. The evaluation consequences of the projected procedure have been contrasted with the results accomplished by the genetic algorithm with the standard static method of apportioning the crossover and mutation probabilities. The evaluation outcomes authorize the preeminence of the new non-linearly escalating procedure over the static tactic of allotting the crossover and mutation prospects for achieving a more optimal yearly profit.