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

journal1422549578_FRONT1

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

Ahmed T, Hamza A, Abdel GA (2008). La commande neuronale de la machine à réluctance variable, Rev. Roum. Sci. Techn.–Électrotechn. Énerg. 53(4):473-482. Bucarest.
ANAMS: National Agency for Meteorology of Senegal (2012).View
Branštetter P, Skotnica M (2000). Application of artificial neural network for speed control of asynchronous motor with vector control, Proceedings of International Conference of Košice, EPE-PEMC, pp. 6-157-6-159.
Chen CT, Chang WD, J Hwu (1997). Direct control of nonlinear dynamical systems using an adaptive single neuron, IEEE Trans. Neural Netw. 2(10):33-40.
Cybenko G (1989). Approximation by superposition of a sigmoidal function”, Math. In: Control Signals System 2nd ed, pp.303-314.
Dreyfus G (2002). Réseaux de neurones: Méthodologies et applications, editions Eyrolles.
Hongmei T, Mancilla-Davida F, Kevin E, Eduard M, Peter J (2012). A cell-to-module-to-acell detailed model for photovoltaic panels. Solar Energy 86(9):2695-2706.
CrossRef
Hagan MT, Demuth HB (1996). Neural network design, Thomson Asia Pte Ltd, 2nd ed.
Koh C, Khouzam K, hoon C Ly, Poo Yong Ng (1994). Simulation and real-time modelling of space photovoltaic systems. In Conference Record of the 24th IEEE Photovoltaic Specialists Conference, 2:2038-2041.
Komi GASSO (2000). Identification des systèmes non-linéaires : approche multi-modèle. PhD these Doctorat soutenue en.
Mohammed S, Djamel EC, Fayçal KM (2007). Commande neuronale inverse des systèmes nonlinéaires, In 4th International Conference on Computer Integrated Manufacturing CIP, 2007 03-04 November.
Norgaard M (1996). System identification and control with neural networks”, Thesis, Institute of automation, Technical University of Denmark.
Panos J, Antsaklis K, Passino M (1993). Introduction to Intelligent and Autonomous Control, Kluwer Academic Publishers, ISBN: 0-7923-9267-1.
PSA: Senegalese-German program (2011). DASTPVPS\SOLARIRR.INS.
Ravinder KK, Shimib SL, Chatterjib S, Fahim A (2014).Modeling of solar pv module and maximum power point tracking using anfis. Renew. Sustain. Energy Rev. 33:602-612.
CrossRef
Rival I, Personnaz L, Dreyfus G (1995). Modélisation, classification et commande, Par réseaux de neurones: principes fondamentaux, Méthodologie de conception et illustrations industrielles, Mécanique Industrielle et Matériaux, no 51 (Septembre 1998).
Ronco E, Gawthrop PJ (1997). Neural networks for modelling and control, Techncal Report CSC-97008, Center for Systems and Control, Glasgow.
Tai P, Ryaciotakiboussalis HA, Tai K (1990). The application of neural networks to control systems: A survey, Signals, systems and Computers, Record Twenty-Fourth Asilomar Conference on Vol. 1.
Vandoorn T, Bert R, Frederik De B, Bart M, Lieven V (2009). A Voltage-Source Inverter for Microgrid Applications with an Inner Current Control Loop and an Outer Voltage Control Loop, International Conference on Renewable Energies, and Power Quality (ICREPQ09) Valencia (Spain), 15th to 17th April.
Wofrance (2012). http://www.wofrance.fr/weather/maps/city.
Wishart MT, Harley RG (1995). Identification and Control of Induction Machines Using Artificial Neural Networks. IEEE Trans. Ind. Applic. 31:3.
CrossRef
Yildirim S (1997). New neural networks for adaptive control of robot manipulators, Neural Networks, International Conference, 3:1727-1731.
Zameer A, Singh SN (2013). Modeling and control of grid connected photovoltaic system: A review. Int. J. Emerg. Technol. Adv. Eng. 3:2250-2459.

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

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