On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions

Wafa Hayder (), Dezso Sera, Emanuele Ogliari and Abderezak Lashab
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Wafa Hayder: Société de Construction et d’Équipement, Gabes 6001, Tunisia
Dezso Sera: Faculty of Science and Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia
Emanuele Ogliari: Department of Energy, Politecnico di Milano, 20156 Milan, Italy
Abderezak Lashab: Department of Energy Technology, Center for Research on Microgrids (CROM), Aalborg University, Pontoppidanstraede 111, DK-9220 Aalborg, Denmark

Energies, 2022, vol. 15, issue 20, 1-15

Abstract: This article analyzes and compares the integration of two different maximum power point tracking (MPPT) control methods, which are tested under partial shading and fast ramp conditions. These MPPT methods are designed by Improved Particle Swarm Optimization (IPSO) and a combination technique between a Neural Network and the Perturb and Observe method (NN-P&O). These two methods are implemented and simulated for photovoltaic systems (PV), where various system responses, such as voltage and power, are obtained. The MPPT techniques were simulated using the MATLAB/Simulink environment. A comparison of the performance of the IPSO and NN-P&O algorithms is carried out to confirm the best accomplishment of the two methods in terms of speed, accuracy, and simplicity.

Keywords: maximum power point tracking (MPPT); improved particle swarm optimization (IPSO); photovoltaic (PV); neural network and perturb and observe method (NN-P&O) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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