I. S. Guerra, Maria, M. U. de Araújo, Fábio, Dhimish, Mahmoud et al. (1 more author) (2021) Assessing Maximum Power Point Tracking Intelligent Techniques on a PV System with a Buck–Boost Converter. Energies. 7453. ISSN 1996-1073
Abstract
Classic and intelligent techniques aim to locate and track the maximum power point of photovoltaic (PV) systems, such as perturb and observe (P&O), fuzzy logic (FL), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFISs). This paper proposes and compares three intelligent algorithms for maximum power point tracking (MPPT) control, specifically fuzzy, ANN, and ANFIS. The modeling of a single-diode equivalent circuit-based 3 kWp PV plant was developed and validated to achieve this purpose. Then, the MPPT techniques were designed and applied to control the buck–boost converter’s switching device of the PV plant. All three methods use the ambient conditions as input variables: solar irradiance and ambient temperature. The proposed methodology comprises the study of the dynamic response for tracking the maximum power point and the power generated of the PV systems, and it was compared to the classic P&O technique under varying ambient conditions. We observed that the intelligent techniques outperformed the classic P&O method in tracking speed, tracking accuracy, and reducing oscillation around the maximum power point (MPP). The ANN technique was the better control algorithm in energy gain, managing to recover up to 9.9% power.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021, The Author(s). |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Electronic Engineering (York) |
Depositing User: | Pure (York) |
Date Deposited: | 09 Nov 2021 12:20 |
Last Modified: | 16 Oct 2024 17:59 |
Published Version: | https://doi.org/10.3390/en14227453 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.3390/en14227453 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:180223 |