Maximum power point tracking for a PV system using support vector machine

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dc.contributor.authorBayarassou, Houria
dc.contributor.authorMegri, Abderrahim Fayçal
dc.contributor.authorBennour, Hachem
dc.date.accessioned2025-04-21T16:42:10Z
dc.date.available2025-04-21T16:42:10Z
dc.date.issued2021
dc.description.abstractSince the maximum power point (MPP) of a photovoltaic system changes with the changes in irradiation and temperature, an appropriate maximum power point tracking (MPPT) controller must be applied in the photovoltaic system. In this article, one of the powerful machine learning, support vector machine (SVM) is used as a predictive model, combining two traditional MPPT methods and the most commonly used: perturb and observation and incremental conductance to force photovoltaic systems to operate more efficiently In different weather conditions. The effectiveness of the proposed method is verified by Mathlab/Simulink simulation.
dc.identifier.urihttp://dspace.univ-oeb.dz:4000/handle/123456789/21953
dc.language.isoen
dc.publisherUniversity of Oum El Bouaghi
dc.subjectPV system ; MPPT: maximum power point tracking; SVM :support vector machine ; P&O: Perturb and Observe ; INC: Incremental Conductance
dc.titleMaximum power point tracking for a PV system using support vector machine
dc.typeArticle
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The 1 ST international conference on electronics, artificial intelligence and technologies ICEAINT 2021