Abstract

One of the major challenges in photovoltaic (PV) systems is extracting the maximum power from the PV array, especially when they operate under partial shading conditions (PSCs). To address this challenge, this paper introduces a novel hybrid maximum power point tracking (MPPT) method based on grey wolf optimization and particle swarm optimization (GWO–PSO) techniques. The developed MPPT technique not only avoids the common disadvantages of conventional MPPT techniques (such as perturb and observe (P&O) and incremental conductance) but also provides a simple and robust MPPT scheme to effectively handle partial shading in PV systems, since it requires only two control parameters, and its convergence to the global maximum power point (GMPP) is independent of the search process's initial conditions. The feasibility and effectiveness of the hybrid GWO–PSO-based MPPT method are verified via a co-simulation technique that combines MATLAB/SIMULINK and PSIM software environments, while comparing its performance against GWO, PSO and P&O based MPPT methods. The simulation results carried out under dynamic environmental conditions have shown the satisfactory effectiveness of the hybrid MPPT method in terms of tracking accuracy, convergence speed to GMPP and efficiency, compared to other methods.

Details

Title
A novel hybrid GWO–PSO-based maximum power point tracking for photovoltaic systems operating under partial shading conditions
Author
Chtita, Smail 1 ; Motahhir, Saad 2 ; El Hammoumi, Aboubakr 3 ; Chouder, Aissa 4 ; Benyoucef, Abou Soufiane 5 ; El Ghzizal, Abdelaziz 6 ; Derouich, Aziz 7 ; Abouhawwash, Mohamed 8 ; Askar, S. S. 9 

 SMBA University, Industrial Technologies and Services Laboratory, EST, Fez, Morocco 
 SMBA University, Engineering, Systems and Applications Laboratory, ENSA, Fez, Morocco 
 SMBA University, Innovative Technologies Laboratory, EST, Fez, Morocco 
 University Mohamed Boudiaf of M’sila, Electrical Engineering Laboratory (LGE), M’Sila, Algeria (GRID:grid.442480.e) (ISNI:0000 0004 0489 9914) 
 University Djilali Bounaama, Khemis Miliana, Khemis Miliana, Algeria (GRID:grid.442455.6) (ISNI:0000 0004 0547 4002) 
 SMBA University, Innovative Technologies Laboratory, EST, Fez, Morocco (GRID:grid.442455.6) 
 SMBA University, Industrial Technologies and Services Laboratory, EST, Fez, Morocco (GRID:grid.442455.6) 
 Mansoura University, Department of Mathematics, Faculty of Science, Mansoura, Egypt (GRID:grid.10251.37) (ISNI:0000000103426662); Michigan State University, Department of Computational Mathematics, Science, and Engineering (CMSE), College of Engineering, East Lansing, USA (GRID:grid.17088.36) (ISNI:0000 0001 2150 1785) 
 King Saud University, Department of Statistics and Operations Research, College of Science, Riyadh, Saudi Arabia (GRID:grid.56302.32) (ISNI:0000 0004 1773 5396) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2679973603
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.