Improving the Yearly Profit of Wind Farm with Artificial Intelligence Technique

Abstract

Owing to the escalating environmental and social problems linked to climate change and the hastily depleting stock of hydrocarbon-based fuels, renewable power generation modes have attained massive prominence. Wind power is an important renewable energy generation technology that contributed to 5% of the planet’s power generation in 2020. However, for sustaining the Paris Agreement targets, the global wind power generation sector necessitates evolving at a fleeter pace. To expand the green switch of the worldwide power generation businesses, wind farms are expected to remain financially more advantageous than fossil fuel-based power plants. The present work focused on elevating the annual profit of wind farms by employing an amended genetic algorithm (GA). A fresh approach to dynamically apportioning the crossover and mutation prospects for a GA-enabled profit growth algorithm was suggested to amplify the capability of the GA. Three dissimilar terrain conditions with diverse obstruction configurations and a randomly generated non-uniform wind flow pattern were used for assessing the competence of the proposed algorithm for profit maximization. The results showed that the annual yields for Terrain Layouts 1, 2 and 3 obtained by the amended GA were higher by 10.34, 5.09 and 0.51%, respectively, than the typical one, which substantiated the superior proficiency of the former.