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

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IJCA Proceedings on Innovations in Computing and Information Technology (Cognition 2015)
© 2015 by IJCA Journal
COGNITION 2015 - Number 4
Year of Publication: 2015
Authors:
Sanjay Yadav
Manoj Kumar
Ranjana Arora

Sanjay Yadav, Manoj Kumar and Ranjana Arora. Article: Maximum Power Point Tracking of Solar Photovoltaic system using Artificial Neural Networks. IJCA Proceedings on Innovations in Computing and Information Technology (Cognition 2015) COGNITION 2015(4):1-5, July 2015. Full text available. BibTeX

@article{key:article,
	author = {Sanjay Yadav and Manoj Kumar and Ranjana Arora},
	title = {Article: Maximum Power Point Tracking of Solar Photovoltaic system using Artificial Neural Networks},
	journal = {IJCA Proceedings on Innovations in Computing and Information Technology (Cognition 2015)},
	year = {2015},
	volume = {COGNITION 2015},
	number = {4},
	pages = {1-5},
	month = {July},
	note = {Full text available}
}

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.

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