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Reseach Article

Maximum Power Point Tracking of Solar Photovoltaic system using Artificial Neural Networks

Published on July 2015 by Sanjay Yadav, Manoj Kumar, Ranjana Arora
Innovations in Computing and Information Technology (Cognition 2015)
Foundation of Computer Science USA
COGNITION2015 - Number 4
July 2015
Authors: Sanjay Yadav, Manoj Kumar, Ranjana Arora
56c56375-3d96-4421-a91b-4251faf56ef5

Sanjay Yadav, Manoj Kumar, Ranjana Arora . Maximum Power Point Tracking of Solar Photovoltaic system using Artificial Neural Networks. Innovations in Computing and Information Technology (Cognition 2015). COGNITION2015, 4 (July 2015), 1-5.

@article{
author = { Sanjay Yadav, Manoj Kumar, Ranjana Arora },
title = { Maximum Power Point Tracking of Solar Photovoltaic system using Artificial Neural Networks },
journal = { Innovations in Computing and Information Technology (Cognition 2015) },
issue_date = { July 2015 },
volume = { COGNITION2015 },
number = { 4 },
month = { July },
year = { 2015 },
issn = 0975-8887,
pages = { 1-5 },
numpages = 5,
url = { /proceedings/cognition2015/number4/21905-2151/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Innovations in Computing and Information Technology (Cognition 2015)
%A Sanjay Yadav
%A Manoj Kumar
%A Ranjana Arora
%T Maximum Power Point Tracking of Solar Photovoltaic system using Artificial Neural Networks
%J Innovations in Computing and Information Technology (Cognition 2015)
%@ 0975-8887
%V COGNITION2015
%N 4
%P 1-5
%D 2015
%I International Journal of Computer Applications
Abstract

Solar energy is clean and renewable source of energy and its decentralized property is appropriate well at the scattered state of the zones with low density of population. The cost of electricity from the solar array system is comparatively more than the electricity from the utility grid. Therefore, it make sense to operate the PV system at maximum efficiency by maximum power point tracking (MPPT)at any given environmental condition. In this work, the neural network (NN) back propagation algorithm is used to control the operation of the PV array for maximum power point extraction. Two error functions are used. The first is classical error function and the second is a modified error function which takes into consideration the derivative of the error function also. The results obtained are compared and discussed in the current study.

References
  1. Ullberg, Oystein. 1998. Stand Alone Power Systems For the future: Optimal Design, Operation & Control of Solar-Hydrogen Systems, Ph. D. Dissertation, NorweiganUniversity of science and technology, Trondheim.
  2. Townsend, T. U. 1989. A Method for estimating the Long-Term Performance of Direct-coupled Photovoltaic systems, MS Thesis, University of Wisconsin, Madison, 1989.
  3. Middle brook. R. D. 1988. Small-Signalmodeling of pulse-width modulatedswitched-mode power converters.
  4. Kisiovski,A, Redl,R. 1994. Sokal,N, Dynamic analysis of switching-mode DC/DC converters.
  5. Kuschewski,J. G. 1993. Application of feed forward neural networks to dynamical system identification and control.
  6. Shireen,W and Arefeen,M. S. 1996. Anutility interactive power electronics interfaceforalternate/renewable energy systems.
  7. Tsai,M. and Tsai,W. I. 1999,Analysis and design of three-phase AC-to-DC converterswith high power factor and near-optimum feed forward.
  8. IEEE Standard for Interconnecting DistributedResources with Electric Power Systems, IEEE Standard 1547, 2003.
  9. Broeck, H W VD,Skudelny,HC,Stanke,GV1988. Analysis and realisation of a pulse widthmodulation based on voltage space vector modulation.
Index Terms

Computer Science
Information Sciences

Keywords

Neural Network Mppt Technique Solar Photovoltaic System