Feasibility of artificial neural network for maximum power point estimation of non crystalline-Si photovoltaic modules

Basit Öğe Kaydı
dc.contributor.authorSyafaruddin
dc.contributor.authorHiyama T.
dc.contributor.authorKaratepe E.
dc.date.accessioned2019-10-26T22:51:38Z
dc.date.available2019-10-26T22:51:38Z
dc.date.issued2009
dc.departmentEge Üniversitesien_US
dc.descriptionCNPq;CAPES;Araucaria FAPEMIG - Brazilian res. funding agencies Found.;COPEL;Itaipu Power Planten_US
dc.description2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09 -- 8 November 2009 through 12 November 2009 -- Curitiba -- 79307en_US
dc.description.abstractSolar cell markets are growing favorably. The emerging non crystalline silicon (c-Si) technologies are starting to make significant in-roads into solar cell markets. The most of the artificial neural network (ANN) have been used in maximum power points tracking applications for c-Si solar cell technology. However, the characteristics of different solar cell technologies at maximum power point (MPP) have different trends in currentvoltage characteristic. In this reason, the investigation of feasibility using neural networks is very important for different solar cell technologies to increase the efficiency of photovoltaic (PV) systems. The paper investigates three different ANN structures, such as radial basis function (RBF), adaptive neurofuzzy inference system (ANFIS) and three layered feed-forward neural network (TFFN) for identification the optimum operating voltage of non c-Si PV modules. These ANN models have been trained and verified for double junction amorphous Si (2j a-Si), triple junction amorphous Si (3j a-Si), Cadmium Indium Diselenide (CIS) and thin film Cadmium Telluride (CdTe) solar cell technologies. The results show that the flexibility of training process, the simplicity of network structure and the accuracy of validation error are important factors to select a suitable ANN model. © 2009 IEEE.en_US
dc.identifier.doi10.1109/ISAP.2009.5352956
dc.identifier.isbn9781424450985
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/ISAP.2009.5352956
dc.identifier.urihttps://hdl.handle.net/11454/20286
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.relation.ispartof2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subject2j a-Sen_US
dc.subject3j a-Sien_US
dc.subjectANFISen_US
dc.subjectANNen_US
dc.subjectCISen_US
dc.subjectRBFen_US
dc.subjectSolar Cellen_US
dc.subjectTFFNen_US
dc.subjectThin film CdTeen_US
dc.titleFeasibility of artificial neural network for maximum power point estimation of non crystalline-Si photovoltaic modulesen_US
dc.typeConference Objecten_US

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu