Title :
Estimating the photovoltaic MPPT by artificial neural network
Author :
Farhat, Soha ; Alaoui, R. ; Kahaji, A. ; Bouhouch, L.
Author_Institution :
EST d´Agadir, ERTAIER, Agadir, Morocco
Abstract :
The approach adopted in this study is to build a model of artificial neural network based on the architecture Multi-layer Perceptron (MLP) whose training is based on practical data. These are measured for a photovoltaic panel (PPV) around data acquisition chain composed of a certain number of sensors including temperature and global solar radiation. The objective is to track, in real time, the maximum power point (MPPT: Maximum Power Point Tracker) by using the model proposed MLP, directly from the Data irradiance namely G and the temperature T. This proposed modeling MLP is validated by using the statements measurements.
Keywords :
data acquisition; maximum power point trackers; neural nets; photovoltaic power systems; power engineering computing; MLP; artificial neural network; data acquisition chain; data irradiance; global solar radiation; maximum power point tracker; multilayer perceptron; photovoltaic MPPT; photovoltaic panel; Artificial neural networks; Data models; MATLAB; Neurons; Training; Artificial neural network; MLP architecture; MPPT; Matlab; Photovoltaic; Power converter; Sigmoid;
Conference_Titel :
Renewable and Sustainable Energy Conference (IRSEC), 2013 International
Conference_Location :
Ouarzazate
Print_ISBN :
978-1-4673-6373-0
DOI :
10.1109/IRSEC.2013.6529641
