Increasing Annual Profit of Wind Farm Using Improved Genetic Algorithm

Abstract

Wind energy, a prominent renewable source of energy, has expanded rapidly in the past few decades. This paper focuses on raising the yearly profit of a possible wind farm in the Kayathar area of India using an enhanced genetic algorithm. Novel dynamic techniques for assigning the probabilities of crossover and mutation operations have been applied for the genetic algorithm-based optimization method along with the conventional static approach. Non-linear functions have been applied for dynamically allocating the crossover and mutation factors for the genetic algorithm-based optimization process. The analysis outcomes of the proposed technique have been compared with the solutions attained by the genetic algorithm with the standard static approach of allocating the crossover and mutation factors. The evaluation outcomes confirm the superiority of the novel non-linearly incrementing methodology over the non-linearly decrementing and static approach of allocating the crossover and mutation probabilities for attaining a more optimal annual profit.