Title: Optimisation of MPPT techniques: conventional, AI-based, and hybrid approaches in photovoltaic systems

Authors: Ballouti Abdelkarim; Hatim Ameziane; Mohamed Chouiekh; Youness El Mourabit; Alia Zakriti

Addresses: National School of Applied Sciences, Abdelmalek Essaadi University, B.P. 2121, Tetouan, Morocco ' Science and Technology for the Engineer Laboratory (LaSTI), National School of Applied Sciences Khouribga (ENSA), Sultan Moulay Slimane Abdelmalek Essaadi University, B.P. 77, 25000 Khouribga, Morocco ' National School of Applied Sciences, Abdelmalek Essaadi University, B.P. 2121, Tetouan, Morocco ' Laboratory TSI, Higher School of Technology of Fès, Sidi Mohammed Ben Abdellah University, B.P. 2427, Route d'Imouzzer, Fes, Morocco ' National School of Applied Sciences, Abdelmalek Essaadi University, B.P. 2121, Tetouan, Morocco

Abstract: Maximum power point tracking (MPPT) techniques are crucial for PV systems, which are sensitive to irradiation and temperature variations. This research introduces a high-performance hybrid approach that boosts efficiency by combining the perturb and observe (P&O) technique with artificial neural networks (ANN) to enhance PV power extraction. The hybrid ANN-P&O algorithm dynamically adjusts the step size based on real-time solar irradiance conditions, making the tracking process more adaptive and efficient. To evaluate this approach, a PV system is simulated under varying weather conditions. The hybrid method's performance is compared to standard P&O and ANN techniques using MATLAB/Simulink simulations. Results highlight the enhanced response time of the hybrid ANN-P&O method (0.16 s), demonstrating a faster and more stable tracking process compared to ANN (0.18 s) and P&O (0.24 s). Additionally, the hybrid ANN-P&O achieves 98% efficiency, outperforming ANN (96%) and P&O (95%). These findings confirm the superiority of hybrid strategies in optimising energy output, ensuring higher power conversion and greater system stability for photovoltaic applications.

Keywords: photovoltaic; PV; maximum power point tracking; MPPT; perturb and observe; P&O; artificial neural network; ANN; DC-DC converter.

DOI: 10.1504/IJPT.2026.152000

International Journal of Powertrains, 2026 Vol.15 No.1, pp.1 - 17

Received: 07 Nov 2024
Accepted: 15 Aug 2025
Published online: 02 Mar 2026 *

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