DKHICHI, Fayrouz Hassan II Casablanca University - Faculty of Sciences and Techniques - Electrical Engineering Department, Laboratory of Electronique, Electrotechnique, Automatique et Traitement de l’Information, Morocco , OUKARFI, Benyounes Hassan II Casablanca University - Faculty of Sciences and Techniques - Electrical Engineering Department, Laboratory of Electronique, Electrotechnique, Automatique et Traitement de l’Information, Morocco , FAKKAR, Abderrahim Hassan II Casablanca University - Faculty of Sciences and Techniques - Electrical Engineering Department, Laboratory of Electronique, Electrotechnique, Automatique et Traitement de l’Information, Morocco , EL KOUARI, Youssef Hassan II Casablanca University - Faculty of Sciences and Techniques - Electrical Engineering Department, Laboratory of Electronique, Electrotechnique, Automatique et Traitement de l’Information, Morocco

Identification of Photovoltaic Cell Model by Neural Network

35373

An accurate parameter identification method of photovoltaic cell model is very helpful to know the behavior of a photovoltaic cell in different meteorological conditions. In this regard, the artificial neural network presents the adequate method that ensures the modeling of the photovoltaic cell characteristic for different values of temperature and irradiance. The present paper presents therefore two neural networks corresponding to the single diode and to the double diodes photovoltaic cell models. Trough the obtained outcomes, the first model provides more accuracy with less complexity of the network unlike the second model entitled under the double diode model, which is more complex and heavy to implement. A further study of the photovoltaic cell behavior is given by the trends of the some obtained electrical parameters according to the investigated temperature and irradiance values.

90

Levenberg , Marquardt , photovoltaic cell , Artificial neural network , Signle diode model , Double diode model

Mediterranean Telecommunications Journal

94