A genetic algorithm optimized ANN-based MPPT algorithm for a stand-alone PV system with induction motor drive

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dc.contributor.authorKulaksiz, Ahmet Afsin
dc.contributor.authorAkkaya, Ramazan
dc.date.accessioned2020-03-26T18:23:30Z
dc.date.available2020-03-26T18:23:30Z
dc.date.issued2012
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractArtificial neural network (ANN) based maximum power point tracking (MPPT) algorithm makes use of the advantages of ANNs such as noise rejection capability and not requiring any prior knowledge of the physical parameters relating to PV system. This paper proposes a genetic algorithm (GA) optimized ANN-based MPPT algorithm implemented in a stand-alone PV system with direct-coupled induction motor drive. The major objective of this design is to eliminate dc-dc, converter and its accompanying losses. Implementing offline ANN in DSP needs optimization of ANN structure to obtain an ideal size. GA optimization was used in this study to determine neuron numbers in multi-layer perceptron neural network. Another objective of this work is to prevent the necessity of the trade-off between the tracking speed and the oscillations around the maximum power point. Hence, varying step size is used in MPPT algorithm and PI-controller is adopted for simple implementation. Simulation and experimental results have been used to demonstrate effectiveness of the proposed method. (C) 2012 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipSelcuk UniversitySelcuk University [2002/226]en_US
dc.description.sponsorshipThis work was supported by Selcuk University scientific research projects coordinate (BAP) under Contract No. 2002/226.en_US
dc.identifier.doi10.1016/j.solener.2012.05.006en_US
dc.identifier.endpage2375en_US
dc.identifier.issn0038-092Xen_US
dc.identifier.issue9en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2366en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.solener.2012.05.006
dc.identifier.urihttps://hdl.handle.net/20.500.12395/27668
dc.identifier.volume86en_US
dc.identifier.wosWOS:000309079800014en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.relation.ispartofSOLAR ENERGYen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectPhotovoltaicsen_US
dc.subjectMaximum power point trackingen_US
dc.subjectArtificial neural networksen_US
dc.subjectGenetic algorithmsen_US
dc.subjectInduction motor driveen_US
dc.titleA genetic algorithm optimized ANN-based MPPT algorithm for a stand-alone PV system with induction motor driveen_US
dc.typeArticleen_US

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