Training Data Optimization for ANNs Using Genetic Algorithms to Enhance MPPT Efficiency of a Stand-Alone PV System

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dc.contributor.authorKulaksız, Ahmet Afşin
dc.contributor.authorAkkaya, Ramazan
dc.date.accessioned2020-03-26T18:31:58Z
dc.date.available2020-03-26T18:31:58Z
dc.date.issued2012
dc.departmentSelçuk Üniversitesien_US
dc.descriptionInternational Symposium on INnovations in Intelligent SysTems and Applications (INISTA) -- JUN 21-24, 2010 -- Kayseri, TURKEYen_US
dc.description.abstractMaximum power point tracking (MPPT) algorithms are used to force photovoltaic (PV) modules to operate at their maximum , power points for all environmental conditions. In artificial neural network (ANN)-based algorithms, the maximum power points are acquired by designing ANN models for PV modules. However, the parameters of PV modules are not always provided by the manufacturer and cannot be obtained readily by the user. Experimental measurements implemented in the overall PV system may be used to obtain the ANN dataset. One drawback of this method is that the generalization ability of the neural network usually degrades and some data reducing the effectiveness of the network may exist. A genetic algorithm can be used to automatically select the important data among all the inputs, resulting in a smaller and more effective dataset. In our study, a genetic algorithm is used to improve the MPPT efficiency of a PV system with induction motor drive by optimizing the input dataset for an ANN model of PV modules. A variable frequency volts-per-Hertz (V/f) control method is applied for speed control of the induction motor, and a space-vector pulse-width modulation (SV-PWM) method is used to operate a 3-phase inverter. Both simulation and experimental results are presented to demonstrate the validation of the method.en_US
dc.description.sponsorshipErciyes Univen_US
dc.identifier.citationKulaksız, A. A., Akkaya, R., (2012). Training Data Optimization for ANNs Using Genetic Algorithms to Enhance MPPT Efficiency of a Stand-Alone PV System. Turkish Journal of Electrical Engineering and Computer Sciences, 20(2), 241-254. Doi:10.3906/elk-1101-1051
dc.identifier.doi10.3906/elk-1101-1051en_US
dc.identifier.endpage254en_US
dc.identifier.issn1300-0632en_US
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage241en_US
dc.identifier.urihttps://dx.doi.org/10.3906/elk-1101-1051
dc.identifier.urihttps://hdl.handle.net/20.500.12395/28584
dc.identifier.volume20en_US
dc.identifier.wosWOS:000300802800006en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.institutionauthorKulaksız, Ahmet Afşin
dc.institutionauthorAkkaya, Ramazan
dc.language.isoenen_US
dc.publisherTUBITAK SCIENTIFIC & TECHNICAL RESEARCH COUNCIL TURKEYen_US
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectPhotovoltaic systemsen_US
dc.subjectartificial neural networksen_US
dc.subjectgenetic algorithmsen_US
dc.subjectspace-vector pulse-width modulationen_US
dc.titleTraining Data Optimization for ANNs Using Genetic Algorithms to Enhance MPPT Efficiency of a Stand-Alone PV Systemen_US
dc.typeArticleen_US

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