Increasing Annual Profit of Wind Farm Using Improved Genetic Algorithm

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  • Prasun Bhattacharjee Ramakrishna Mission Shilpapitha
  • Somenath Bhattacharya Jadavpur University



Annual Profit, Dynamic Allocation, Genetic Algorithm, Profit Maximization, Windfarm


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. 


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Author Biographies

Prasun Bhattacharjee, Ramakrishna Mission Shilpapitha

Department of Mechanical Engineering, India

Somenath Bhattacharya, Jadavpur University

Department of Mechanical Engineering, India


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How to Cite

Bhattacharjee, P., & Bhattacharya, S. (2023). Increasing Annual Profit of Wind Farm Using Improved Genetic Algorithm. International Journal of Advanced Natural Sciences and Engineering Researches, 7(4), 203–209.

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