Maximum Power Point tracking for a stand-alone photovoltaic system using Artificial Neural Network

Maximum Power Point tracking for a stand-alone photovoltaic system using Artificial Neural Network

This paper presents an intelligent method to extract the maximum power from the photovoltaic panel using artificial neural network (ANN). The inputs data required for training the ANN controller are obtained from real weather conditions and the desired output is obtained from perturb and observe (P&...

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Main Authors: Ghedhab Nabila, Youcefettoumi Fatiha, Loukriz Abdelhamid, Jouama Allaeddine
Format: Article
Language:English
Published: EDP Sciences 2020-01-01
Series:E3S Web of Conferences
Online Access:https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/12/e3sconf_peee2020_01007.pdf
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spelling doaj-d49ba5ea6c584a96b7447cdfb4c16e702021-04-02T16:04:50ZengEDP SciencesE3S Web of Conferences2267-12422020-01-011520100710.1051/e3sconf/202015201007e3sconf_peee2020_01007Maximum Power Point tracking for a stand-alone photovoltaic system using Artificial Neural NetworkGhedhab Nabila0Youcefettoumi Fatiha1Loukriz Abdelhamid2Jouama Allaeddine3Department of Electronic Engineering University of Science and Technology Houari Boumedien AlgiersDepartment of Electronic Engineering University of Science and Technology Houari Boumedien AlgiersDepartment of Electrical Engineering High school of Polytechnic ENP AlgiersDepartment of Electronic Engineering University of Science and Technology Houari Boumedien AlgiersThis paper presents an intelligent method to extract the maximum power from the photovoltaic panel using artificial neural network (ANN). The inputs data required for training the ANN controller are obtained from real weather conditions and the desired output is obtained from perturb and observe (P&O) method. The proposed model is capable to improve the dynamic response and steady-state performance of the system, provides an accurate identification of the optimal operating point and an accurate estimation of the maximum power from the photovoltaic panels. The proposed ANN model is compared with conventional P&O model and shown that ANN controller could increase the power output by approximately 20%. The system is simulated and studied using MATLAB software.https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/12/e3sconf_peee2020_01007.pdf
collection DOAJ
language English
format Article
sources DOAJ
author Ghedhab Nabila
Youcefettoumi Fatiha
Loukriz Abdelhamid
Jouama Allaeddine
spellingShingle Ghedhab Nabila
Youcefettoumi Fatiha
Loukriz Abdelhamid
Jouama Allaeddine
Maximum Power Point tracking for a stand-alone photovoltaic system using Artificial Neural Network
E3S Web of Conferences
author_facet Ghedhab Nabila
Youcefettoumi Fatiha
Loukriz Abdelhamid
Jouama Allaeddine
author_sort Ghedhab Nabila
title Maximum Power Point tracking for a stand-alone photovoltaic system using Artificial Neural Network
title_short Maximum Power Point tracking for a stand-alone photovoltaic system using Artificial Neural Network
title_full Maximum Power Point tracking for a stand-alone photovoltaic system using Artificial Neural Network
title_fullStr Maximum Power Point tracking for a stand-alone photovoltaic system using Artificial Neural Network
title_full_unstemmed Maximum Power Point tracking for a stand-alone photovoltaic system using Artificial Neural Network
title_sort maximum power point tracking for a stand-alone photovoltaic system using artificial neural network
publisher EDP Sciences
series E3S Web of Conferences
issn 2267-1242
publishDate 2020-01-01
description This paper presents an intelligent method to extract the maximum power from the photovoltaic panel using artificial neural network (ANN). The inputs data required for training the ANN controller are obtained from real weather conditions and the desired output is obtained from perturb and observe (P&O) method. The proposed model is capable to improve the dynamic response and steady-state performance of the system, provides an accurate identification of the optimal operating point and an accurate estimation of the maximum power from the photovoltaic panels. The proposed ANN model is compared with conventional P&O model and shown that ANN controller could increase the power output by approximately 20%. The system is simulated and studied using MATLAB software.
url https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/12/e3sconf_peee2020_01007.pdf
work_keys_str_mv AT ghedhabnabila maximumpowerpointtrackingforastandalonephotovoltaicsystemusingartificialneuralnetwork
AT youcefettoumifatiha maximumpowerpointtrackingforastandalonephotovoltaicsystemusingartificialneuralnetwork
AT loukrizabdelhamid maximumpowerpointtrackingforastandalonephotovoltaicsystemusingartificialneuralnetwork
AT jouamaallaeddine maximumpowerpointtrackingforastandalonephotovoltaicsystemusingartificialneuralnetwork
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