PAGE PERSONNELLE



Dr. AZZEDDINE Hocine Abdelhak عزالدين الحسين عبدالحق
Faculté des Sciences et de la Technologie
Département Electrotéchnique
Grade : Maitre de conférence classe A
Numéro de Téléphone :0798960872
Adresse électronique institutionnel :hocine.azzeddine@univ-mascara.dz
Adresse électronique personnel :hocineazzeddine@yahoo.fr
Adresse postale :
Cité 70 log. Cité Said . Bloc C/01
Laboratoire des sciences et techniques de l'eau
Lien Google Scholar :https://scholar.google.com/AZZEDDINE_Hocine Abdelhak
Lien Researchgate: www.researchgate.net/profile/AZZEDDINE_Hocine Abdelhak
Mots Clés de Recherche : Energies renouvelables, matériaux électrotechniques, intelligence artificielle, Contrôle


Ouvrages Publications Projets Communications










Publications

  • Characteristics and Electrical Parameters of Silicon Nanowires (SiNWs) Solar Nanocells
  • 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
  • Date de publication : 0000-00-00
  • Fuel cell grid connected system with active power generation and reactive power compensation features
  • La revue : Przegląd Elektrotechniczny
    Domaine : Energies renouvelables
    Mots Clés : Fuel cell
    Auteur : H.A.AZZEDDINE , D.CHAOUCH1 , M.Berka1 , M.Hebali A.Larbaoui1 , M.Tioursi
    Issn : Eissn : vol : 96, Num : , pp : 124-127
  • Date de publication : 0000-00-00
  • 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
  • Date de publication : 0000-00-00
  • AN OFFLINE TRAINED ARTIFICIAL NEURAL NETWORK TO PREDICT A PHOTOVOLTAIC PANEL MAXIMUM POWER POINT
  • 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.





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