Adaptive maximum power point tracking using neural networks for a photovoltaic systems according grid

Introduction. This article deals with the optimization of the energy conversion of a grid-connected photovoltaic system. The novelty is to develop an intelligent maximum power point tracking technique using artificial neural network algorithms. Purpose. Intelligent maximum power point tracking techn...

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Autores principales:	H. Sahraoui, H. Mellah, S. Drid, L. Chrifi-Alaoui
Formato:	article
Lenguaje:	EN RU UK
Publicado:	National Technical University "Kharkiv Polytechnic Institute" 2021
Materias:	grid-connected artificial neural network adaptive modified perturbation and observation artificial neural network-maximum power point tracking Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/e5c28242eeb8426fa3ba3f7f305cc747
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spelling	oai:doaj.org-article:e5c28242eeb8426fa3ba3f7f305cc7472021-12-02T19:06:25ZAdaptive maximum power point tracking using neural networks for a photovoltaic systems according grid2074-272X2309-340410.20998/2074-272X.2021.5.08https://doaj.org/article/e5c28242eeb8426fa3ba3f7f305cc7472021-10-01T00:00:00Zhttp://eie.khpi.edu.ua/article/view/242511https://doaj.org/toc/2074-272Xhttps://doaj.org/toc/2309-3404Introduction. This article deals with the optimization of the energy conversion of a grid-connected photovoltaic system. The novelty is to develop an intelligent maximum power point tracking technique using artificial neural network algorithms. Purpose. Intelligent maximum power point tracking technique is developed in order to improve the photovoltaic system performances under the variations of the temperature and irradiation. Methods. This work is to calculate and follow the maximum power point for a photovoltaic system operating according to the artificial intelligence mechanism is and the latter is used an adaptive modified perturbation and observation maximum power point tracking algorithm based on function sign to generate an specify duty cycle applied to DC-DC converter, where we use the feed forward artificial neural network type trained by Levenberg-Marquardt backpropagation. Results. The photovoltaic system that we chose to simulate and apply this intelligent technique on it is a stand-alone photovoltaic system. According to the results obtained from simulation of the photovoltaic system using adaptive modified perturbation and observation – artificial neural network the efficiency and the quality of the production of energy from photovoltaic is increased. Practical value. The proposed algorithm is validated by a dSPACE DS1104 for different operating conditions. All practice results confirm the effectiveness of our proposed algorithm.H. SahraouiH. MellahS. DridL. Chrifi-AlaouiNational Technical University "Kharkiv Polytechnic Institute"articlegrid-connected artificial neural networkadaptive modified perturbation and observationartificial neural network-maximum power point trackingElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENRUUKElectrical engineering & Electromechanics, Iss 5, Pp 57-66 (2021)
institution	DOAJ
collection	DOAJ
language	EN RU UK
topic	grid-connected artificial neural network adaptive modified perturbation and observation artificial neural network-maximum power point tracking Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	grid-connected artificial neural network adaptive modified perturbation and observation artificial neural network-maximum power point tracking Electrical engineering. Electronics. Nuclear engineering TK1-9971 H. Sahraoui H. Mellah S. Drid L. Chrifi-Alaoui Adaptive maximum power point tracking using neural networks for a photovoltaic systems according grid
description	Introduction. This article deals with the optimization of the energy conversion of a grid-connected photovoltaic system. The novelty is to develop an intelligent maximum power point tracking technique using artificial neural network algorithms. Purpose. Intelligent maximum power point tracking technique is developed in order to improve the photovoltaic system performances under the variations of the temperature and irradiation. Methods. This work is to calculate and follow the maximum power point for a photovoltaic system operating according to the artificial intelligence mechanism is and the latter is used an adaptive modified perturbation and observation maximum power point tracking algorithm based on function sign to generate an specify duty cycle applied to DC-DC converter, where we use the feed forward artificial neural network type trained by Levenberg-Marquardt backpropagation. Results. The photovoltaic system that we chose to simulate and apply this intelligent technique on it is a stand-alone photovoltaic system. According to the results obtained from simulation of the photovoltaic system using adaptive modified perturbation and observation – artificial neural network the efficiency and the quality of the production of energy from photovoltaic is increased. Practical value. The proposed algorithm is validated by a dSPACE DS1104 for different operating conditions. All practice results confirm the effectiveness of our proposed algorithm.
format	article
author	H. Sahraoui H. Mellah S. Drid L. Chrifi-Alaoui
author_facet	H. Sahraoui H. Mellah S. Drid L. Chrifi-Alaoui
author_sort	H. Sahraoui
title	Adaptive maximum power point tracking using neural networks for a photovoltaic systems according grid
title_short	Adaptive maximum power point tracking using neural networks for a photovoltaic systems according grid
title_full	Adaptive maximum power point tracking using neural networks for a photovoltaic systems according grid
title_fullStr	Adaptive maximum power point tracking using neural networks for a photovoltaic systems according grid
title_full_unstemmed	Adaptive maximum power point tracking using neural networks for a photovoltaic systems according grid
title_sort	adaptive maximum power point tracking using neural networks for a photovoltaic systems according grid
publisher	National Technical University "Kharkiv Polytechnic Institute"
publishDate	2021
url	https://doaj.org/article/e5c28242eeb8426fa3ba3f7f305cc747
work_keys_str_mv	AT hsahraoui adaptivemaximumpowerpointtrackingusingneuralnetworksforaphotovoltaicsystemsaccordinggrid AT hmellah adaptivemaximumpowerpointtrackingusingneuralnetworksforaphotovoltaicsystemsaccordinggrid AT sdrid adaptivemaximumpowerpointtrackingusingneuralnetworksforaphotovoltaicsystemsaccordinggrid AT lchrifialaoui adaptivemaximumpowerpointtrackingusingneuralnetworksforaphotovoltaicsystemsaccordinggrid
_version_	1718377182220255232

Adaptive maximum power point tracking using neural networks for a photovoltaic systems according grid

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