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Support vector machine for MPPT efficiency improvement in photovoltaic system

This paper is aimed at enhancing the effectiveness of maximum power point tracking (MPPT) controller for PV systems. The Support Vector Machine (SVM) is proposed to accomplish the MPPT controller. Furthermore, the proposed SVM technique has been validated with hypothetical, the perturbation and obse...

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Main Authors: Kareim, A.A., Mansor, M.B.
Format: Article
Language:en_US
Published: 2017
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spelling my.uniten.dspace-59322018-01-18T07:03:47Z Support vector machine for MPPT efficiency improvement in photovoltaic system Kareim, A.A. Mansor, M.B. This paper is aimed at enhancing the effectiveness of maximum power point tracking (MPPT) controller for PV systems. The Support Vector Machine (SVM) is proposed to accomplish the MPPT controller. Furthermore, the proposed SVM technique has been validated with hypothetical, the perturbation and observation (P&O), and incremental conductance (IC) algorithms. We have also implemented MATLAB models for PV module, theoretical, SVM, P&O, and IC algorithms. The optimum voltage of the PV system has been predicted by the enhanced MPPT by employing the SVM method, for the purpose of extracting the maximum power point (MPP). The solar radiation and room temperature of the modeled PV module are the two types of inputs employed by the SVM technique, and ultimately the optimum voltage of the PV system is the output of the SVM model. The results of the validation have revealed that, the proposed SVM technique has minimized Root Mean Square Error (RMSE) and performs far better than P&O and IC methods. Thus, it has been proved that, the proposed SVM method is efficient enough as against the P&O and IC methods, and extracts high power from PV system. © 2013 Praise Worthy Prize S.r.l. - All rights reserved. 2017-12-08T07:41:18Z 2017-12-08T07:41:18Z 2013 Article https://pure.uniten.edu.my/en/publications/support-vector-machine-for-mppt-efficiency-improvement-in-photovo en_US Support vector machine for MPPT efficiency improvement in photovoltaic system. International Review of Automatic Control, 6(2), 177-182
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/
language en_US
description This paper is aimed at enhancing the effectiveness of maximum power point tracking (MPPT) controller for PV systems. The Support Vector Machine (SVM) is proposed to accomplish the MPPT controller. Furthermore, the proposed SVM technique has been validated with hypothetical, the perturbation and observation (P&O), and incremental conductance (IC) algorithms. We have also implemented MATLAB models for PV module, theoretical, SVM, P&O, and IC algorithms. The optimum voltage of the PV system has been predicted by the enhanced MPPT by employing the SVM method, for the purpose of extracting the maximum power point (MPP). The solar radiation and room temperature of the modeled PV module are the two types of inputs employed by the SVM technique, and ultimately the optimum voltage of the PV system is the output of the SVM model. The results of the validation have revealed that, the proposed SVM technique has minimized Root Mean Square Error (RMSE) and performs far better than P&O and IC methods. Thus, it has been proved that, the proposed SVM method is efficient enough as against the P&O and IC methods, and extracts high power from PV system. © 2013 Praise Worthy Prize S.r.l. - All rights reserved.
format Article
author Kareim, A.A.
Mansor, M.B.
spellingShingle Kareim, A.A.
Mansor, M.B.
Support vector machine for MPPT efficiency improvement in photovoltaic system
author_facet Kareim, A.A.
Mansor, M.B.
author_sort Kareim, A.A.
title Support vector machine for MPPT efficiency improvement in photovoltaic system
title_short Support vector machine for MPPT efficiency improvement in photovoltaic system
title_full Support vector machine for MPPT efficiency improvement in photovoltaic system
title_fullStr Support vector machine for MPPT efficiency improvement in photovoltaic system
title_full_unstemmed Support vector machine for MPPT efficiency improvement in photovoltaic system
title_sort support vector machine for mppt efficiency improvement in photovoltaic system
publishDate 2017
_version_ 1644493803543855104
score 13.222552

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