Efficiency improvement of the maximum power point tracking for PV systems using support vector machine technique

Ameer A. Kareim, Muhamad Mansor

Research output: Contribution to journalConference article

Abstract

The aim of this paper is to improve efficiency of maximum power point tracking (MPPT) for PV systems. The Support Vector Machine (SVM) was proposed to achieve the MPPT controller. The theoretical, the perturbation and observation (P&O), and incremental conductance (IC) algorithms were used to compare with proposed SVM algorithm. MATLAB models for PV module, theoretical, SVM, P&O, and IC algorithms are implemented. The improved MPPT uses the SVM method to predict the optimum voltage of the PV system in order to extract the maximum power point (MPP). The SVM technique used two inputs which are solar radiation and ambient temperature of the modeled PV module. The results show that the proposed SVM technique has less Root Mean Square Error (RMSE) and higher efficiency than P&O and IC methods.

Original languageEnglish
Article number012099
JournalIOP Conference Series: Earth and Environmental Science
Volume16
Issue number1
DOIs
Publication statusPublished - 01 Jan 2013
Event26th IAHR Symposium on Hydraulic Machinery and Systems - Beijing, China
Duration: 19 Aug 201223 Aug 2012

Fingerprint

improvement of efficiency
support vector machine
solar radiation
perturbation
temperature
method

All Science Journal Classification (ASJC) codes

  • Environmental Science(all)
  • Earth and Planetary Sciences(all)

Cite this

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abstract = "The aim of this paper is to improve efficiency of maximum power point tracking (MPPT) for PV systems. The Support Vector Machine (SVM) was proposed to achieve the MPPT controller. The theoretical, the perturbation and observation (P&O), and incremental conductance (IC) algorithms were used to compare with proposed SVM algorithm. MATLAB models for PV module, theoretical, SVM, P&O, and IC algorithms are implemented. The improved MPPT uses the SVM method to predict the optimum voltage of the PV system in order to extract the maximum power point (MPP). The SVM technique used two inputs which are solar radiation and ambient temperature of the modeled PV module. The results show that the proposed SVM technique has less Root Mean Square Error (RMSE) and higher efficiency than P&O and IC methods.",
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Efficiency improvement of the maximum power point tracking for PV systems using support vector machine technique. / Kareim, Ameer A.; Mansor, Muhamad.

In: IOP Conference Series: Earth and Environmental Science, Vol. 16, No. 1, 012099, 01.01.2013.

Research output: Contribution to journalConference article

TY - JOUR

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AU - Kareim, Ameer A.

AU - Mansor, Muhamad

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N2 - The aim of this paper is to improve efficiency of maximum power point tracking (MPPT) for PV systems. The Support Vector Machine (SVM) was proposed to achieve the MPPT controller. The theoretical, the perturbation and observation (P&O), and incremental conductance (IC) algorithms were used to compare with proposed SVM algorithm. MATLAB models for PV module, theoretical, SVM, P&O, and IC algorithms are implemented. The improved MPPT uses the SVM method to predict the optimum voltage of the PV system in order to extract the maximum power point (MPP). The SVM technique used two inputs which are solar radiation and ambient temperature of the modeled PV module. The results show that the proposed SVM technique has less Root Mean Square Error (RMSE) and higher efficiency than P&O and IC methods.

AB - The aim of this paper is to improve efficiency of maximum power point tracking (MPPT) for PV systems. The Support Vector Machine (SVM) was proposed to achieve the MPPT controller. The theoretical, the perturbation and observation (P&O), and incremental conductance (IC) algorithms were used to compare with proposed SVM algorithm. MATLAB models for PV module, theoretical, SVM, P&O, and IC algorithms are implemented. The improved MPPT uses the SVM method to predict the optimum voltage of the PV system in order to extract the maximum power point (MPP). The SVM technique used two inputs which are solar radiation and ambient temperature of the modeled PV module. The results show that the proposed SVM technique has less Root Mean Square Error (RMSE) and higher efficiency than P&O and IC methods.

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