Ahmed Ibrahim
National Research Tomsk Polytechnic University

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Maximum power point tracking of partially shading PV system using cuckoo search algorithm Ahmed Ibrahim; Raef Aboelsaud; Sergey Obukhov
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 10, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v10.i2.pp1081-1089

Abstract

This paper presents a cuckoo search (CS) algorithm for determining the global maximum power point (GMPP) tracking of photovoltaic (PV) under partial shading conditions (PSC). The conventional methods are fail to track the GMPP under PSC, which decrease the reliability of the power system and increase the system losses. The performance of the CS algorithm is compared with perturb and observe (P&O) algorithm for different cases of operations of PV panels under PSC. The CS algorithm used in this work to control directly the duty cycle of the DC-DC converter without proportional integral derivative (PID) controller. The proposed CS model can track the GMPP very accurate with high efficiency in less time under different conditions as well as in PSC.
Maximum power point tracking of photovoltaic array using fuzzy logic control Djaafar Toumi; Djilani Benattous; Ahmed Ibrahim; Bedida Tarek; Messaoud Zobeidi; Khelaifa Fethi; Hicham Serhoud; Yacine Labbi; Raef Aboelsaud; Sergey Obukhov; Mohamed Tayeb Boussabeur
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 13, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v13.i4.pp2440-2449

Abstract

This research introduces the simulation of photovoltaic (PV) array to track the peak point (MPPT) using fuzzy logic control. Therefore, real time simulation is performed in MATLAB/Simulink based on a PV model, boost converter and fuzzy logic-based tracker. A comparative study is carried out against perturb and observe (P&O) controller. The fuzzy logic technique based tracker can successfully track the maximum power point very fast and has precise control when compared to the P&O algorithm. The overall we conclude that the MPPT using the fuzzy logic technique takes a fast response and can improve the performance of the PV system.
Study of neural controller based MPPT in comparison with P&O for PV systems Djaafar Toumi; Mourad Tiar; Abir Boucetta; Ikram Boucetta; Ahmed Ibrahim
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i1.pp797-808

Abstract

This study investigated the performance of two prominent maximum power point tracking (MPPT) strategies: the established perturb and observe (P&O) technique and an artificial neural network (ANN)-based controller. Through simulations conducted in MATLAB/Simulink, a 50 W photovoltaic (PV) array was evaluated under dynamic irradiance and temperature variations. Notably, data generated by the P&O system served as the training dataset for the ANN model. The simulation results indicate that the ANN controller effectively and accurately identifies the PV system’s optimal operating point even amidst fluctuating environmental conditions. When compared to the conventional P&O method, the ANN approach demonstrated superior characteristics, including a significantly faster response, diminished oscillations around the maximum power point, and enhanced tracking accuracy during rapid environmental shifts. These findings underscore the substantial potential of ANN-based MPPT strategies for improving both the efficiency and operational stability of photovoltaic power systems.