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Article Number - E90B70F55617


Vol.13(1), pp. 1-7 , January 2018
https://doi.org/10.5897/IJPS2017-4696
ISSN: 1992-1950


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Full Length Research Paper

A comparative study of maximum power point tracker approaches based on artificial neural network and fuzzy controllers



Moustapha Sene
  • Moustapha Sene
  • Electronics IT Telecommunications and Renewable Energy Laboratory (LEITER), Gaston Berger University, Route de Ngallele, P. O. Box 234 Saint-Louis, West Africa, Senegal.
  • Google Scholar
Fatou Ndiaye
  • Fatou Ndiaye
  • Electronics IT Telecommunications and Renewable Energy Laboratory (LEITER), Gaston Berger University, Route de Ngallele, P. O. Box 234 Saint-Louis, West Africa, Senegal.
  • Google Scholar
Marie E. Faye
  • Marie E. Faye
  • Electronics IT Telecommunications and Renewable Energy Laboratory (LEITER), Gaston Berger University, Route de Ngallele, P. O. Box 234 Saint-Louis, West Africa, Senegal.
  • Google Scholar
Saliou Diouf
  • Saliou Diouf
  • Information Processing Laboratory (LTI), Higth Polytechnic School, Cheikh Anta Diop University, P. O. Box 5085 Dakar–Fann, West Africa, Senegal.
  • Google Scholar
Amadou S. Maiga
  • Amadou S. Maiga
  • Electronics IT Telecommunications and Renewable Energy Laboratory (LEITER), Gaston Berger University, Route de Ngallele, P. O. Box 234 Saint-Louis, West Africa, Senegal.
  • Google Scholar







 Received: 12 November 2017  Accepted: 20 December 2017  Published: 16 January 2018

Copyright © 2018 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0


The performances of a photovoltaic (PV) module connected to a load through a conversion stage (chopper, inverter) are linked to the average electricity output including the delivered power. Nevertheless, the efficiency depends on atmospheric parameters as temperature, irradiance, and wind speed. To make electrical power available, Maximum Power Point Trackers (MPPT) algorithms are developed to keep up the PV module at optimal operating point with regard to climatic variations. This paper proposes an assessment of Artificial Neural Networks model based on MultiLayer Perceptron (MLP) and Radial Basis Function (RBF). A comparative study with an Adaptive Neuro-Fuzzy Inference System and a hybrid neural network RBF/MLP is done using measured data to optimize the maximum power point of a photovoltaic generator.
 
Key words: Multilayer perceptron, radial basis function, maximum power point trackers, neuro-fuzzy.

Bahgat ABG, Helwa NH, Ahmad GE, El Shenawy ET (2005). Maximum power point tracking controller for PV systems using neural networks. Renew. Energy 30:1257-1265.
Crossref

 

Elgendy MA, Atkinson DJ, Zahawi B (2016). Experimental investigation of the incremental conductance maximum power point tracking algorithm at high perturbation rates. IET Renew. Power Gen. 10(2):133-139.
Crossref

 
 

Elgendy MA, Zahawi B, Atkinson DJ (2012). Assessment of Perturb and Observe MPPT Algorithm Implementation Techniques for PV Pumping Applications. IEEE Trans. Sustain. Energy 3(1):21-34.
Crossref

 
 

Elgharbi A, Mezghani D, Mami A (2012). A Maximum power point tracking method based on artificial neural network for a PV system. Int. J. Adv. Eng. Technol. IJAET.

 
 

Enany MA (2017). Cuckoo search–based maximum power point tracking controller for PV water pumping system. ‎J. Renew. Sustain. Energy 9:063501.
Crossref

 
 

Haskin S (2005). Neural networks: A comprehensive foundation. San Francisco Pearson Education.

 
 

Jang JS, Anfis R (1993). Adaptive-Network-based Fuzzy Inference System. IEEE Trans. Syst. Man Cybernetics 23:665-685.
Crossref

 
 

Jang JS, Anfis R (1995). Neuro-Fuzzy Modeling and Control. Proc. IEEE 83(3):378-406.
Crossref

 
 

Mastromauro RA, Liserre M, Dell'Aquila A (2012). Control Issues in Single Stage Photovoltaic Systems: MPPT, Current and Voltage Control. IEEE Trans. Ind. Inf. 8(2):241-254.
Crossref

 
 

Ndiaye F, Sène M, Beye M, Seidou Maiga A (2015). Effects of climatic conditions on a polycrystalline photovoltaic module in Niger. Int. Lett. Chem. Phys. Astron. 55:60-66.
Crossref

 
 

Reisi AR, Moradi MH, Jamasb S (2013). Classification and comparison of maximum power point tracking techniques for photovoltaic system: A review. J. Renew. Sust. Energy Rev. 19:433-443.
Crossref

 
 

Saloux E, Teyessdou A, Sorin M (2011). Explicit model of photovoltaic panel to determine voltages and current at the maximum power point. Solar energy 85:713-722.
Crossref

 
 

Vafaei S, Rezvani A, Gandomkar M, Izadbakhsh M (2015). Enhancement of grid-connected photovoltaic system using ANFIS-GA under different circumstances. Front. Energy 9(3):322-334.
Crossref

 
 

Zou Y, Yu Y, Zhang Y, Luc J (2012). MPPT Control for PV Generation System Based on an Improved Inccond Algorithm. Elsevier, Int. Workshop Info. Electron. Eng. 105-109.
Crossref

 

 


APA Séne, M., Ndiaye, F., Faye, M. E., Diouf, S., & Maïga, A. S. (2018). A comparative study of maximum power point tracker approaches based on artificial neural network and fuzzy controllers. International Journal of Physical Sciences, 13(1), 1-7.
Chicago Moustapha S&ene, Fatou Ndiaye, Marie E. Faye, Saliou Diouf and  Amadou S. Maïga. "A comparative study of maximum power point tracker approaches based on artificial neural network and fuzzy controllers." International Journal of Physical Sciences 13, no. 1 (2018): 1-7.
MLA Moustapha Seacute;ne, et al. "A comparative study of maximum power point tracker approaches based on artificial neural network and fuzzy controllers." International Journal of Physical Sciences 13.1 (2018): 1-7.
   
DOI https://doi.org/10.5897/IJPS2017-4696
URL http://academicjournals.org/journal/IJPS/article-abstract/E90B70F55617

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