CFP last date
20 May 2024
Reseach Article

Intelligent Algorithm for Maximum Power Tracking of Photovoltaic Energy System

by A. H. Mohamed, A.m. Nassar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 115 - Number 20
Year of Publication: 2015
Authors: A. H. Mohamed, A.m. Nassar
10.5120/20271-2680

A. H. Mohamed, A.m. Nassar . Intelligent Algorithm for Maximum Power Tracking of Photovoltaic Energy System. International Journal of Computer Applications. 115, 20 ( April 2015), 36-41. DOI=10.5120/20271-2680

@article{ 10.5120/20271-2680,
author = { A. H. Mohamed, A.m. Nassar },
title = { Intelligent Algorithm for Maximum Power Tracking of Photovoltaic Energy System },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 115 },
number = { 20 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 36-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume115/number20/20271-2680/ },
doi = { 10.5120/20271-2680 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:55:50.432971+05:30
%A A. H. Mohamed
%A A.m. Nassar
%T Intelligent Algorithm for Maximum Power Tracking of Photovoltaic Energy System
%J International Journal of Computer Applications
%@ 0975-8887
%V 115
%N 20
%P 36-41
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Tracking the maximum power point (MPP) of a photovoltaic array is an essential operation for any PV system. Thus, changing the environmental conditions causes different maximum power point (MPP) driving for non-uniform power feeding for the load. So, researchers have developed many MPPT algorithms to maximize and stabilize the power derived for the load. But, these algorithms still suffer from some limitations such as high cost, complexity and sometime instability with rapid changes in the environmental conditions. The proposed system introduces integration between the artificial neural networks (ANN) and the fuzzy logic controller (FLC) for achieving MPPT. It can improve the performance of the MPPT algorithm for obtaining a stable maximum power derived for the loads. The proposed MPPT algorithm has been applied for a PV system feeds the power for a camp that exposes the radiation site. The results obtained from the suggested system have proved its significant success for the practical applications.

References
  1. V. Salas, E. Olias, A. Barrado, A. Lazaro, "Review of the maximum power point tracking algorithms for standalone photovoltaic systems", Solar Energy Materials & Solar Cells, Vol. 90, pp. 1555-1578, 2011.
  2. C. R. Sullivan, M. J. Powers, "A high-efficiency maximum power point tracker for photovoltaic arrays in a solarpoweredrace vehicle", Proceedings of the IEEE Power Electronics Specialists Conference, pp. 574-580, 2013.
  3. J. A. Gow amd C. D. Manning, "Controller Arrangement For Boost Converter Systems Sourced From Solar Photovoltaic arrays or other maximum power sources", IEE Proceedings - Electric Power Applications, Vol. 147, pp. 15-20, 2010.
  4. K. H. Hussein, I. Muta, T. Hoshino, M. Osakada, "Maximum Photovoltaic Power Yracking: An Algorithm For Rapidly Changing Atmosphere Conditions", Proceedings of the IEE - Generation, Transmission, and Distribution, Vol. 142, pp. 59-64, 2005.
  5. M. Veerachary, T. Senjyu, K. Uezato, "Neural Network based Maximum Power Point Tracking of Coupled-Inductor Interleaved-boost Converter Supplied PV System using Fuzzy Controller", IEEE Transactions on Industrial Electronics, Vol. 50, pp. 749-758, 2012.
  6. A. A. Kulaks?z, "ANN-based control of a PV system with maximum power point tracker and SVM inverter", Ph. D, Dissertation, Sel?cuk University Graduate School of Natural and Applied Sciences, Department of Electrical and Electronics Engineering, Konya, Turkey, 2009.
  7. A. B. G. Bahgat, N. H. Helwa, G. E. Ahmad, E. T. El Shenawy, "Maximum power point tracking controller for PV systems using neural networks", Renewable Energy, Vol. 30, pp. 1257-1268, 2011.
  8. S. T. Hiyama and E. Karatepe, "Investigation of ANN performance for tracking the optimum points of PV module under partially shaded conditions", 2010 Conference Proceedings IPEC, pp. 1186-1191, 2010.
  9. V. Salas, E. Olias, A. Barrado, and A. Lazaro, "Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems," Sol. Energy Mater. Sol. Cells, Vol. 90, No. 11, pp. 1555–1578, Jul. 2006.
  10. Z. Cheng, Z. Pang, Y. Liu and P. Xue, "An adaptive solar Photovoltaic array reconfiguration methods based on Fuzzy control", in 2010 8th World Congress on Intelligent Control and Automation (WCICA), pp. 176-181, 2010.
  11. F. Z. Brill, D. E. Brown, W. N. Martin, "Fast genetic selection of features for neural network classifiers", IEEE Transactions on Neural Networks, Vol. 3, pp. 324-328, 2012.
  12. H. Patel and V. Agarwal, "Maximum Power Point Tracking Scheme for PV Systems Operating Under Partially Shaded Conditions," IEEE Transactions On Industrial Electronics, Vol. 55, No. 4, pp. 1689–1698, April 2008.
  13. A. A. Kulaks?z, R. Akkaya, "Training data optimization for ANNs using genetic algorithms to enhance MPPT efficiency of a stand-alone PV system", International Symposium on Innovations in Intelligent Systems and Applications, pp. 523-527, 2010.
  14. J. Yang, V. Honavar, "Feature subset selection using a genetic algorithm", IEEE Intelligent Systems, Vol. 13, pp. 44-49, 2012.
  15. L. Kottas,S. Boutalis and D. Karlis, "New Maximum Power Point Tracker for PV Arrays Using Fuzzy Controller in Close Cooperation With Fuzzy Cognitive Networks", IEEE Transaction on Energy Conversion, VOL. 21, NO. 3, September 2006, pp. 793-803.
  16. A. M. A. Mahmoud, H. M. Mashaly, S. A. Kandil, H. El Khashab, and M. N. F. Nashed, "Fuzzy Logic Implementation For Photovoltaic Maximum Power Tracking", Proceedings 9th IEEE International Workshop on Robot and Human Interactive Communication, pp. 155 –160, 2000.
  17. Dipti Bawa, C. Y. Patil, "Fuzzy Control based Solar Tracker using Arduino Uno ", International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 12, June 2013
  18. T. Esram, P. L. Chapman, "Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques," IEEE Transactions on Energy Conversion, vol. 22, no. 2, pp. 439-449, June 2007.
  19. M. A. Elgendy, B. Zahawi, and D. J. Atkinson. "Assessment of Perturb and Observe MPPT algorithm implementation techniques for PV pumping applications". IEEE transactions on sustainable energy, Vol 3, No 1, pp. 21-33, 2012.
  20. M. T. Makhloufi, M. S. Khireddine, Y. Abdessemed, A. Boutarfa, " Tracking Power Photovoltaic System using Artificial Neural Network Control Strategy, I. J. Intelligent Systems and Applications, Vol. 12, pp. 17-26, 2014.
Index Terms

Computer Science
Information Sciences

Keywords

PV Systems Maximum Power Point Tracking (MPPT) Power Converters Artificial Neural Networks Fuzzy Logic Controller.