An Evaluation on Wind Energy Potential Using Multi-Objective Optimization Based Non-Dominated Sorting Genetic Algorithm III

Wind energy is an abundant renewable resource that has been extensively used worldwide in recent years. The present work proposes a new Multi-Objective Optimization (MOO) based genetic algorithm (GA) model for wind system. proposed consists of non-dominated sorting which focuses to maximize the power extraction turbine, minimize cost generating energy, and lifetime battery. Additionally, performance characteristics turbine battery storage system (BESS) are analyzed specifically torque, current, voltage, state charge (SOC), internal resistance. complete analysis carried out MATLAB/Simulink platform. simulated results compared with existing optimization techniques such as single-objective, multi-objective, non-dominating GA II (Genetic Algorithm-II). From observed results, (NSGA III) offers superior notably higher output torque rate, lower speed variation, reduced cost, lesser degradation rate This result attested fact tool can extract from self-excited induction generator (SEIG) when conventional tool.