La revue : JOURNAL OF NANO- AND ELECTRONIC PHYSICS
Domaine : Material
Mots Clés : Auteur : M. Hebali, M. Bennaoum, H.A. Azzeddine, B. Ibari, M. Benzohra, D. Chalabi
Issn : 2077-6772 Eissn : vol : 12, Num : 06, pp : 1-4
Résume : This article presents a control of a three-phase low voltage grid connected fuel cell system which participating in the improvement of the quality of energy at the connection point by ensuring the reactive energy compensation, the active power control and the harmonic filtering
functionalities. A p-q theory based control has been developed to control the injected fuel cell active power and to allow the system to provide the reactive energy compensation function. The system is structured around a proton exchange membrane (PEM) fuel cell system and a three-phase
voltage inverter.
Photovoltaic Panel modeling using a RBFN artificial neural network.
La revue : Acta Electrotechnica
Domaine : Energies renouvelables, Réseaux de neurones artificiels
Mots Clés : PV, modélisation, RBF
Auteur : H.A.Azzeddine, M.Tioursi. D.chaouch
Issn : 1841-3323 Eissn : vol : 54, Num : 6, pp : 1-3
La revue : Rev. Roum. Sci. Techn.– Électrotechn. et Énerg.
Domaine : Energies renouvelables, Réseaux de neurones artificiels
Mots Clés : PV, MPPT, RBF
Auteur : HOCINE ABDELHAK AZZEDDINE, MUSTAPHA TIOURSI, DJAMEL-EDDINE CHAOUCH, BRAHIM KHIARI
Issn : ?0035-4066 Eissn : vol : 61, Num : , pp : 255-257
Date de publication : 2016-07-16
Résume : In this work, we develop a radial basis artificial neural network to predict the voltage and the current at maximum power point of a photovoltaic panel under different cell temperature and solar irradiance conditions. For training the proposed artificial neural network, we generate a group of maximum power points defined by their corresponding current and voltage values using
the photovoltaic panel single diode model. To ensure the validity of the artificial neural network, we compare the obtained results to those obtained by using the photovoltaic panel single diode model for cell temperature and solar irradiance conditions other than those used for the training phase. Results show that the developed artificial neural network can predict accurately and quickly the current and the voltage of the photovoltaic panel at the maximum power point for any cell temperature and solar irradiance conditions.
