Application of new modeling and control for grid connected photovoltaic systems based on artificial intelligence

Application of new modeling and control for grid connected photovoltaic systems based on artificial intelligence

TITLE: Application of new modeling and control for grid connected photovoltaic systems based on artificial intelligence

AUTHOR: Alphousseyni Ndiaye; Lamine Thiaw; Gustave Sow

DATE OF PUBLICATION: 29 January 2015

ISBN: 1993-8225


This review-paper focuses on the development the intelligent technology for modelling (Multi-Model Approach (MMA)) and control (Artificial Neural Networks Controller) of grid connected photovoltaic energy conversion system. This approach (MMA) is based on a black box modeling. A database consists of input variables (sunshine, temperature and voltage at the terminals of photovoltaic generator (PVG) and output (PVG current) is obtained by characterization of a photovoltaic module Sharp installed type at the “Polytechnic Higher School” (PHS) in Dakar in March 2012. Indeed 70% of this database is used to train the multi-model and 30% of the database is reserved for validation of the multi-model. The proposed model has a correlation of 89% and a Nash criterion (NS) average of 75.65%. Learning is performed with oil operating area. Each area of ​​operation is made by a local affine model structure and function of validity sigmoid. These results show the good performance of the proposed model. Control design of a single phase grid-connected photovoltaic (PV) system including the PV array and the electronic power conditioning (PCS) system, based on Artificial Neural Networks Controller (ANNC) is presented. The developed controller is compared with a Proportional Integral (PI) controller through computer simulation. The obtained results show that the NNC have faster response and lower THD without overshoots.


Key words: Black box modelling, photovoltaic generator, inverter, maximum power point tracking (MPPT), neural network controller.

Alphousseyni Ndiaye

Lamine Thiaw

Gustave Sow

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Received: 25 September 2014  Accepted: 16 December 2014  Published: 29 January 2015

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