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Τίτλος: FPGA-based implementation of a real time photovoltaic module simulator
Συγγραφείς: Mekki, Hamza 
Mellit, Adel 
Kalogirou, Soteris A. 
Messai, Adnane 
Furlan, G. 
Major Field of Science: Engineering and Technology
Field Category: Environmental Engineering
Λέξεις-κλειδιά: Neural networks;Photovoltaic module;FPGA;Prediction;Simulation;VDHL
Ημερομηνία Έκδοσης: 2010
Πηγή: Progress in Photovoltaics: Research and Applications, 2010, vol. 18, no. 2, pp. 115-127
Volume: 18
Issue: 2
Start page: 115
End page: 127
Περιοδικό: Progress in Photovoltaics: Research and Applications 
Περίληψη: An implementation of an intelligent photovoltaic module on reconfigurable Field Programmable Gate Array (FPGA) is described in this paper. An experimental database of meteorological data (irradiation and temperature) and output electrical generation data of a Photovoltaic (PV) module (current and voltage) under variable climate condition is used in this study. Initially, an Artificial Neural Network (ANN) is developed under Matlab/Similuk, environment for modeling the PV module. The inputs of the ANN–PV module are the global solar irradiation and temperature while the outputs are the current and voltage generated from the PV-module. Subsequently, the optimal configuration of the ANN model (ANN–PV module) is written and simulated under the Very High Description Language (VHDL) and ModelSim. The synthesized architecture by ModelSim is then implemented on an FPGA device. The designed MLP-photovoltaic module permits the evaluation of performance of the PV module using only environmental parameters and involves less computational effort. The device can also be used for predicting the output electrical energy from the PV module and for a real time simulation in specific climatic conditions
URI: https://hdl.handle.net/20.500.14279/4397
ISSN: 10627995
DOI: 10.1002/pip.950
Rights: © Wiley
Type: Article
Affiliation: Blida University 
Jijel University 
University of Technology 
CRNB Ain Oussera 
International Center for Theoretical Physics 
Publication Type: Peer Reviewed
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