Université de Bejaia, Faculté de Technologie, Laboratoire de Maitrise des Energies Renouvelables, 06000 Bejaia, Algerie, Ali Berboucha, Said Aissou, Université de Bejaia, Faculté de Technologie, Laboratoire de Maitrise des Energies Renouvelables, 06000 Bejaia, Algerie, Kamel Djermouni, Université de Bejaia, Faculté de Technologie, Laboratoire de Maitrise des Energies Renouvelables, 06000 Bejaia, Algerie, Elyazid Amirouche, Université de Bejaia, Faculté de Technologie, Laboratoire de Maitrise des Energies Renouvelables, 06000 Bejaia, Algerie
This paper introduces an intelligent Maximum Power Point Tracking (MPPT) technique for photovoltaic (PV) systems. The proposed method enhances conventional fuzzy logic MPPT by optimizing fuzzy membership functions using genetic algorithms. The system utilizes a three-level Active Neutral-Point Clamped (ANPC) inverter, a prominent topology within multilevel converter architectures. The application focus is a PV water pumping system comprising PV panels, a boost chopper, the ANPC inverter, induction motors, and a pump. The optimized fuzzy logic MPPT extracts maximum power from the PV panels, while Space Vector Pulse Width Modulation (SVPWM) controls the inverter with capacitor voltage balancing using redundant vectors. Simulations conducted in the MATLAB/Simulink environment validate the proposed system's performance.