LAPSE:2023.8825
Published Article
LAPSE:2023.8825
On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions
February 24, 2023
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
improved particle swarm optimization (IPSO), maximum power point tracking (MPPT), neural network and perturb and observe method (NN-P&O), photovoltaic (PV)
Suggested Citation
Hayder W, Sera D, Ogliari E, Lashab A. On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions. (2023). LAPSE:2023.8825
Author Affiliations
Hayder W: Société de Construction et d’Équipement, Gabes 6001, Tunisia [ORCID]
Sera D: Faculty of Science and Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia [ORCID]
Ogliari E: Department of Energy, Politecnico di Milano, 20156 Milan, Italy [ORCID]
Lashab A: Department of Energy Technology, Center for Research on Microgrids (CROM), Aalborg University, Pontoppidanstraede 111, DK-9220 Aalborg, Denmark [ORCID]
Journal Name
Energies
Volume
15
Issue
20
First Page
7668
Year
2022
Publication Date
2022-10-17
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en15207668, Publication Type: Journal Article
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LAPSE:2023.8825
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https://doi.org/10.3390/en15207668
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Feb 24, 2023
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