On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions
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...
| Principais autores: | , , , |
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| Formato: | Artigo |
| Idioma: | English |
| Publicado em: |
MDPI AG
2022-10-01
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| coleção: | Energies |
| Assuntos: | |
| Acesso em linha: | https://www.mdpi.com/1996-1073/15/20/7668 |
| _version_ | 1827650435411869696 |
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| author | Wafa Hayder Dezso Sera Emanuele Ogliari Abderezak Lashab |
| author_facet | Wafa Hayder Dezso Sera Emanuele Ogliari Abderezak Lashab |
| author_sort | Wafa Hayder |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T20:16:12Z |
| format | Article |
| id | doaj.art-19e8d5e8dc244e488864b5e20bb83b1c |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T20:16:12Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-19e8d5e8dc244e488864b5e20bb83b1c2023-11-23T23:58:40ZengMDPI AGEnergies1996-10732022-10-011520766810.3390/en15207668On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric ConditionsWafa Hayder0Dezso Sera1Emanuele Ogliari2Abderezak Lashab3Société de Construction et d’Équipement, Gabes 6001, TunisiaFaculty of Science and Engineering, Queensland University of Technology, Brisbane, QLD 4000, AustraliaDepartment of Energy, Politecnico di Milano, 20156 Milan, ItalyDepartment of Energy Technology, Center for Research on Microgrids (CROM), Aalborg University, Pontoppidanstraede 111, DK-9220 Aalborg, DenmarkThis 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.https://www.mdpi.com/1996-1073/15/20/7668maximum power point tracking (MPPT)improved particle swarm optimization (IPSO)photovoltaic (PV)neural network and perturb and observe method (NN-P&O) |
| spellingShingle | Wafa Hayder Dezso Sera Emanuele Ogliari Abderezak Lashab On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions Energies maximum power point tracking (MPPT) improved particle swarm optimization (IPSO) photovoltaic (PV) neural network and perturb and observe method (NN-P&O) |
| title | On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions |
| title_full | On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions |
| title_fullStr | On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions |
| title_full_unstemmed | On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions |
| title_short | On Improved PSO and Neural Network P&O Methods for PV System under Shading and Various Atmospheric Conditions |
| title_sort | on improved pso and neural network p o methods for pv system under shading and various atmospheric conditions |
| topic | maximum power point tracking (MPPT) improved particle swarm optimization (IPSO) photovoltaic (PV) neural network and perturb and observe method (NN-P&O) |
| url | https://www.mdpi.com/1996-1073/15/20/7668 |
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