A Relative Analysis of Genetic Algorithm and Binary Particle Swarm Optimization for Finding the Optimal Cost of Wind Power Generation in Tirumala Area of India

Abstract

Although India presently holds the global fourth-biggest instated Wind Power Generation (WPG) capability, it necessitates advancing more rapidly to satisfy the rising energy requirement of its evolving economy while restraining the consequential greenhouse gas emission. To accomplish the impressive target of setting up 140 GW WPG competence by 2030 as proposed by the Government of India, a greater number of financially viable wind farms are required to function all over the country without further ado. This paper focuses on finding the optimal cost for WPG in the Tirumala area of Andhra Pradesh. Genetic Algorithm (GA) and Binary Particle Swarm Optimization (BPSO) have been employed concurrently with four randomly chosen terrain conditions. The research outcomes demonstrate the superior capability of BPSO to attain the most optimal cost of energy.

Increasing Annual Profit of Wind Farm Using Improved Genetic Algorithm

“…(1) Where P stands for the engrossed energy, ρ signifies the air density, A denotes the swept area, v symbolizes the inbound wind speed, Cp signifies the Betz limit and θ is the angular yaw fault [20]. WPG businesses can remain economical through consummate manipulating of the generation cost [21]. The existing study is focused to expand the yearly profit of a WPG unit [22].…”

Improving the Yearly Profit of Wind Farm with Artificial Intelligence Technique

“…where P denotes the power obtainable for withdrawal by WT; ρ signifies the air density; A represents the swept area; ϑ stands for the airspeed; Cp symbolizes the Betz coefficient; and θ specifies the alignment error (Wu and Wang, 2012;Bhattacharjee et al, 2021). The current research was directed to boost the annual profit of wind farms.…”

“…Internationally, studies have been conducted to lessen the WPG expense and realize the carbon neutrality target proposed in the 2015 Paris Accord (Bhattacharjee et al, 2021). Because of the calculating competence, artificial intelligence (AI) has been utilized to resolve different engineering optimization challenges including the WPG outlay minimization problem (Jana and Bhattacharjee, 2017;Duggirala et al, 2018;Bhattacharjee et al, 2021). A neural network-abetted genetic algorithm (GA) was implemented to foresee WPG outlay (Huang, 2007).…”