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Intelligent Controller based Maximum Power Point Tracking for Solar PV System

by R.Ramaprabha, B.L. Mathur
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 10
Year of Publication: 2011
Authors: R.Ramaprabha, B.L. Mathur
10.5120/1717-2303

R.Ramaprabha, B.L. Mathur . Intelligent Controller based Maximum Power Point Tracking for Solar PV System. International Journal of Computer Applications. 12, 10 ( January 2011), 37-41. DOI=10.5120/1717-2303

@article{ 10.5120/1717-2303,
author = { R.Ramaprabha, B.L. Mathur },
title = { Intelligent Controller based Maximum Power Point Tracking for Solar PV System },
journal = { International Journal of Computer Applications },
issue_date = { January 2011 },
volume = { 12 },
number = { 10 },
month = { January },
year = { 2011 },
issn = { 0975-8887 },
pages = { 37-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume12/number10/1717-2303/ },
doi = { 10.5120/1717-2303 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:01:20.131186+05:30
%A R.Ramaprabha
%A B.L. Mathur
%T Intelligent Controller based Maximum Power Point Tracking for Solar PV System
%J International Journal of Computer Applications
%@ 0975-8887
%V 12
%N 10
%P 37-41
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Solar photovoltaic system performance depends on environmental conditions. Solar photovoltaic panel is a power source having nonlinear internal resistance. As the intensity of light falling on the panel varies, its voltage as well as its internal resistance both varies. To extract maximum power from the panel, the load resistance should be equal to the internal resistance of the panel. This paper analyses the improved modelling of solar PV module and proposes a genetic algorithm (GA) based maximum power point tracking. The GA optimized values are used to train the artificial neural network (ANN). The MPPT is simulated and studied using MATLAB software.

References
  1. C. Hua and C. Shen, “Comparative Study of Peak Power Tracking Techniques for solar Storage System”, IEEE Applied Power Electronics Conference and Exposition Proceedings, Vo1.2, pp.679- 683, 1988.
  2. K.H.Hussein, I. Muta, T. Hoshino and M. Osakada, “Maximum Photovoltaic Power Tracking: An Algorithm for Rapidly Changing Atmospheric Conditions,” IEE Proceedings on Generation, Transmission and Distribution, Vol.142, No.1, pp.59-64, January 1995.
  3. Brambilla, ‘liew Approach U1 Photovoltaic Arrays Maximum Power Point Tracking”, Proceedings of 35th IEEE Power Electronics Specialists Conference, vol. 2, G. 632-637., 1998.
  4. D.P. H u h and M.E. Ropp, “Comparative Study of Maximum Power Point tacking Algorithm Using an Experimental, Programmable, Maximum Power Point Tracking Test Bed”, Proceedings of 28th IEEE Photovoltaic Specialists Conference, pp. 1699-1702, 2000.
  5. T. Hiyama and K. Kitabayashi, “Neural Network Based Estimation of Maximum Power Generation from PV Module Using Environment Information”, IEEE Transactiom on Energy, Conversion, Vol. 12, NO. 3, pp.241-247, September 1997.
  6. T. Hiyama, S. Kouzuma, T. Imakubo, and T.H. Ortmeyer, “Evaluation of Neural Network Based Real Time Maximum Power Tracking Controller Far PV System”, IEEE Transaction On Energv Conversion, Vol.10, No3,pp.543-548, Sept. 1995.
  7. T. Hiyama, S. Kauuma, and T. Imakubo ,“Identification Of Optimal Operating Point Of PV Modules Using Neural Network for Real Time Maximum Power Tracking Control”, IEEE Transaction on Energv Conversion, Vol.10, No. 2 , pp. 360-367, June 1995.
  8. A. de Medeiros Torres, F.L.M. Antunes, and F.S. dos Reis, “An Artificial Neural Network-Based Real Time Maximum Power Tracking Controller For Connecting a PV System to the Grid”, Proceeding of IEEE the 24th Annual Conference on Industrial Electronics Society , Vol. 1 , pp. 554 -558, 1998.
  9. A. AI-Amoudi and L. Zhang, “Application of Radial Basis Function Networks For Solar-Array Modelling And Maximum Power-Point Prediction”, IEE Proceeding - Generation, Transmission and Distribution, Vol. 147, No. 5,pp. 310-316,Sept. 2000.
  10. R.Ramaprabha and Dr.B.L.Mathur,”A Technique to extract maximum Power from Photovoltaic Panels”,Proc. of IEEE Int. Conf. on Recent Advancements and Applications of Computer in Electrical Engineering, pp. 447 –449, Bikaner, Rajastan, India, Mar. 24-25, 2007
  11. S Geoff Walker, “Evaluating MPPT Converter Topologies using a MATLAB PV Model”.
  12. .Duffie.J.A, and Beckman.W.A.(2006), Solar Engineering of Thermal Processes. John Wiley Sons, 3rd edition.
  13. Marcelo Gradella Villalva, Jonas Rafael Gazoli and Ernesto Ruppert Filho (2009),”Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays”,IEEE Transactions On Power Electronics,Vol.24, No.5,pp 1198-1208.
  14. Engin Karatepe, Mutlu Boztepe and Metin Colak, “Development of suitable model for characterizing photovoltaic arrays with shaded solar cells”, Solar Energy, 2007, pp 329-340.
  15. Goldberg, D.E. , “Genetic Algorithms in Search, Optimization and Machine Learning”, Addison-Wesley, Reading, Massachusetts, Harlow, England, 1989.
Index Terms

Computer Science
Information Sciences

Keywords

Solar PV system MPPT GA ANN MATLAB

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