In EDS ansehen

A New Maximum Power Point Tracking With a Combined Particle Swarm Optimization–Biogeography‐Based Optimization Algorithm for Photovoltaic System.

Gespeichert in:
Bibliographische Detailangaben
Titel: A New Maximum Power Point Tracking With a Combined Particle Swarm Optimization–Biogeography‐Based Optimization Algorithm for Photovoltaic System.
Autoren: Aboutorabi, Seyed Mohammad Mehdi H. S., Sarvi, Mohammad
Quelle: Energy Science & Engineering; Jul2025, Vol. 13 Issue 7, p3714-3726, 13p
Schlagwörter: PARTICLE swarm optimization, PHOTOVOLTAIC power systems, SOLAR panels, MAXIMUM power point trackers, SOLAR energy, MATHEMATICAL optimization
Abstract: The amount of power produced by a solar panel depends on the intensity of radiation and surrounding temperature. Optimizing the performance of photovoltaic systems requires the operation of solar panels at the maximum power point (MPP). In the present paper, a novel maximum power point tracking (MPPT) method is introduced based on an intelligent algorithm. The proposed method, called hybrid MPSO‐MBBO, combines modified biogeography‐based optimization (MBBO) and Modified Particle Swarm Optimization (MPSO) algorithms. The performance of the presented algorithm is compared with perturb and observe (P&O) and genetic algorithm (GA) as well as MPSO and MBBO. The effectiveness of the proposed method is further verified by experimental and simulation results in a typical photovoltaic system. The system under study includes a solar panel, an MPPT controller, and a DC–DC converter. To assess the accuracy of the proposed ‎ method, algorithms were implemented by the microcontroller STM32F407VGT6. The results showed that the MBBO algorithm had a higher speed response and the MPSO algorithm resulted in better accuracy, therefore, a combination of the two algorithms was used to track the MPP so that the MPSO algorithm is executed when the irradiance is uniform and the MBBO algorithm is executed when the irradiance has rapid changes. [ABSTRACT FROM AUTHOR]
Copyright of Energy Science & Engineering is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Datenbank: Complementary Index
Beschreibung
Abstract:The amount of power produced by a solar panel depends on the intensity of radiation and surrounding temperature. Optimizing the performance of photovoltaic systems requires the operation of solar panels at the maximum power point (MPP). In the present paper, a novel maximum power point tracking (MPPT) method is introduced based on an intelligent algorithm. The proposed method, called hybrid MPSO‐MBBO, combines modified biogeography‐based optimization (MBBO) and Modified Particle Swarm Optimization (MPSO) algorithms. The performance of the presented algorithm is compared with perturb and observe (P&O) and genetic algorithm (GA) as well as MPSO and MBBO. The effectiveness of the proposed method is further verified by experimental and simulation results in a typical photovoltaic system. The system under study includes a solar panel, an MPPT controller, and a DC–DC converter. To assess the accuracy of the proposed ‎ method, algorithms were implemented by the microcontroller STM32F407VGT6. The results showed that the MBBO algorithm had a higher speed response and the MPSO algorithm resulted in better accuracy, therefore, a combination of the two algorithms was used to track the MPP so that the MPSO algorithm is executed when the irradiance is uniform and the MBBO algorithm is executed when the irradiance has rapid changes. [ABSTRACT FROM AUTHOR]
ISSN:20500505
DOI:10.1002/ese3.70130