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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Indonesian Journal of Electrical Engineering and Computer Science
Belhadj Mohammed
University of Tahri Mohammed
Control & Systems Engineering Electrical & Electronics Engineering

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Design and modeling of optical reflectors for a PV panel adapted by MPPT control Belhadj Mohammed; Boufeldja Kadri; Nasri Abdelfatah; Benlaria Ismail
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp653-660

Abstract

Due to the highly non-linear electrical characteristics of photovoltaic generators (PVGs), the efficiency of PV systems can be improved by forcing the GPV to operate at their maximum power point (MPP). In this article, we are interested in concentrating Photovoltaic design to improve the output current of the panelwhile maintaining the DCDC boost element, after presenting the basic structure of Boost DC-DC converter, which shows the existence of a limitation on the voltage gain for this converter. In order to meet the specifications (high voltage gain and low ripple of the input current), existing structures will be presented that are able to provide a high voltage gain (Photovoltaic concentration) compared to another structure
Neuro-evolutionary genetic algorithm for global MPPT under partial shading conditions: a comparative analysis with PSO Benlaria Ismail; Laidi Abdallah; Fenniche Ayoub; Belhadj Mohammed
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i2.pp1499-1509

Abstract

Maximizing power extraction from photovoltaic (PV) systems is crucial for their overall efficiency. However, under partial shading conditions (PSCs), the power-voltage curve shows several points of maximum power. This phenomenon often leads to traditional maximum power point tracking (MPPT) algorithms getting stuck at suboptimal local peaks, resulting in substantial energy losses. To solve this, we introduce a novel neuro-evolutionary genetic algorithm (NEGA) for global MPPT. This hybrid algorithm integrates a neural network to intelligently guide the evolutionary search process, improving its GMPP tracking. The performance of the NEGA controller is rigorously compared against the widely used particle swarm optimization (PSO) algorithm via MATLAB/Simulink simulations across various irradiance scenarios. Results under severe PSCs demonstrate NEGA's superior tracking efficiency of 98.69%, far exceeding PSO's 76.02%. Moreover, NEGA achieves a faster convergence time of 0.1 s under dynamic irradiance, compared to 0.6s for PSO. The study concludes that NEGA is a robust and highly efficient solution for global MPPT, ensuring maximum power harvesting from PV systems under challenging operating conditions.
Co-Authors Benlaria Ismail Benlaria Ismail Boufeldja Kadri Fenniche Ayoub Laidi Abdallah Nasri Abdelfatah