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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Chakib, Mohssine
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Computer Science & IT Engineering

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Optimization of maximum power point tracking in wind energy systems: a comparative study of ant colony and genetic algorithms Mrabet, Najoua; Benzazah, Chirine; Chakib, Mohssine; Ziraoui, Adil; El Akkary, Ahmed; Laaroussi, Najma
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp399-411

Abstract

This research focuses on optimizing maximum power point tracking (MPPT) in wind energy conversion systems (WECS) using ant colony optimization (ACO) and genetic algorithm (GA). The study evaluates these two metaheuristic techniques to optimize the parameters of a proportional integral-derivative (PID) controller in order to maximize power output in a permanent magnet synchronous generator (PMSG)-based system. Simulations conducted in MATLAB/Simulink show that both ACO and GA effectively enhance MPPT performance by improving power output, DC bus voltage regulation, and torque stability. The results demonstrate the potential of metaheuristic algorithms to optimize wind energy conversion efficiency and support sustainable energy development.
Co-Authors Benzazah, Chirine El Akkary, Ahmed Laaroussi, Najma Mrabet, Najoua Ziraoui, Adil