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An improved maximum power point tracking controller for PV systems using artificial neural network; [Ulepszona metoda ?ledzenia maksymalnej mocy systemu fotowoltaicznego z wykorzystaniem sieci neuronowej]

This paper presents an improved maximum power point tracking (MPPT) controller for PV systems. An Artificial Neural Network and the classical P&O algorithm were employed to achieve this objective. MATLAB models for a neural network, PV module, and the classical P&O algorithm are developed. H...

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Main Authors: Younis M.A., Khatib T., Najeeb M., Mohd Ariffin A.
Other Authors: 56501517900
Format: Article
Published: 2023
Subjects:
ANN
MPPT
P and O algorithm
PV systems
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spelling my.uniten.dspace-295222023-12-28T14:30:20Z An improved maximum power point tracking controller for PV systems using artificial neural network; [Ulepszona metoda ?ledzenia maksymalnej mocy systemu fotowoltaicznego z wykorzystaniem sieci neuronowej] Younis M.A. Khatib T. Najeeb M. Mohd Ariffin A. 56501517900 31767521400 55052092300 16400722400 ANN MPPT P and O algorithm PV systems This paper presents an improved maximum power point tracking (MPPT) controller for PV systems. An Artificial Neural Network and the classical P&O algorithm were employed to achieve this objective. MATLAB models for a neural network, PV module, and the classical P&O algorithm are developed. However, the developed MPPT uses the ANN to predict the optimum voltage of the PV system in order to extract the maximum power point (MPP). The developed ANN has a feedback propagation configuration and it has four inputs which are solar radiation, ambient temperature, and the temperature coefficients of Isc and Voc of the modeled PV module. Meanwhile, the optimum voltage of the PV system is the output of the developed ANN. Based on the results; the response of the proposed MPPT controller is faster than the classical P&O algorithm. Moreover, the average tracking efficiency of the developed algorithm was 95.51% as compared to 85.99% of the classical P&O algorithm. Such developed controller increases the conversion efficiency of a PV system. Final 2023-12-28T06:30:20Z 2023-12-28T06:30:20Z 2012 Article 2-s2.0-84857755382 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84857755382&partnerID=40&md5=d84e0303b0536b5ac8640afab1e4c3d5 https://irepository.uniten.edu.my/handle/123456789/29522 88 3 B 116 121 Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic ANN
MPPT
P and O algorithm
PV systems
spellingShingle ANN
MPPT
P and O algorithm
PV systems
Younis M.A.
Khatib T.
Najeeb M.
Mohd Ariffin A.
An improved maximum power point tracking controller for PV systems using artificial neural network; [Ulepszona metoda ?ledzenia maksymalnej mocy systemu fotowoltaicznego z wykorzystaniem sieci neuronowej]
description This paper presents an improved maximum power point tracking (MPPT) controller for PV systems. An Artificial Neural Network and the classical P&O algorithm were employed to achieve this objective. MATLAB models for a neural network, PV module, and the classical P&O algorithm are developed. However, the developed MPPT uses the ANN to predict the optimum voltage of the PV system in order to extract the maximum power point (MPP). The developed ANN has a feedback propagation configuration and it has four inputs which are solar radiation, ambient temperature, and the temperature coefficients of Isc and Voc of the modeled PV module. Meanwhile, the optimum voltage of the PV system is the output of the developed ANN. Based on the results; the response of the proposed MPPT controller is faster than the classical P&O algorithm. Moreover, the average tracking efficiency of the developed algorithm was 95.51% as compared to 85.99% of the classical P&O algorithm. Such developed controller increases the conversion efficiency of a PV system.
author2 56501517900
author_facet 56501517900
Younis M.A.
Khatib T.
Najeeb M.
Mohd Ariffin A.
format Article
author Younis M.A.
Khatib T.
Najeeb M.
Mohd Ariffin A.
author_sort Younis M.A.
title An improved maximum power point tracking controller for PV systems using artificial neural network; [Ulepszona metoda ?ledzenia maksymalnej mocy systemu fotowoltaicznego z wykorzystaniem sieci neuronowej]
title_short An improved maximum power point tracking controller for PV systems using artificial neural network; [Ulepszona metoda ?ledzenia maksymalnej mocy systemu fotowoltaicznego z wykorzystaniem sieci neuronowej]
title_full An improved maximum power point tracking controller for PV systems using artificial neural network; [Ulepszona metoda ?ledzenia maksymalnej mocy systemu fotowoltaicznego z wykorzystaniem sieci neuronowej]
title_fullStr An improved maximum power point tracking controller for PV systems using artificial neural network; [Ulepszona metoda ?ledzenia maksymalnej mocy systemu fotowoltaicznego z wykorzystaniem sieci neuronowej]
title_full_unstemmed An improved maximum power point tracking controller for PV systems using artificial neural network; [Ulepszona metoda ?ledzenia maksymalnej mocy systemu fotowoltaicznego z wykorzystaniem sieci neuronowej]
title_sort improved maximum power point tracking controller for pv systems using artificial neural network; [ulepszona metoda ?ledzenia maksymalnej mocy systemu fotowoltaicznego z wykorzystaniem sieci neuronowej]
publishDate 2023
_version_ 1806424277545648128
score 13.222552

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