Neural network based solar cell model
Küçük Resim Yok
Tarih
2006
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pergamon-Elsevier Science Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
This paper presents a neural network based approach for improving the accuracy of the electrical equivalent circuit of a photovoltaic module. The equivalent circuit parameters of a PV module mainly depend on solar irradiation and temperature. The dependence on environmental factors of the circuit parameters is investigated by using a set of current-voltage curves. It is shown that the relationship between them is nonlinear and cannot be easily expressed by any analytical equation. Therefore, the neural network is utilized to overcome these difficulties. The neural network is trained once by using some measured current-voltage curves, and the equivalent circuit parameters are estimated by only reading the samples of solar irradiation and temperature very quickly without solving any nonlinear implicit equations that is necessary in conventional methods. To verify the proposed model, an experimental set up is installed. The comparison between the measured values and the proposed model results shows higher accuracy than the conventional model for all operating conditions. (c) 2005 Elsevier Ltd. All rights reserved.
Açıklama
Anahtar Kelimeler
photovoltaic module, equivalent circuit parameter, artificial neural network, I-V characteristics, modeling
Kaynak
Energy Conversion and Management
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
47
Sayı
09.Oct
