Neural Fuzzy Networks for Optimal MPPT Control in PV Powered AC Loads

dc.contributor.author	Della, K. M.
dc.contributor.author	Midoun, A.
dc.date.accessioned	2018-07-22T08:08:41Z
dc.date.available	2018-07-22T08:08:41Z
dc.date.issued	2005-01-01
dc.identifier.issn	1815-3852
dc.identifier.uri	https://journal.uob.edu.bh:443/handle/123456789/405
dc.description.abstract	Renewable energies are being more popular and viewed in some cases as a viable alternative to conventional sources of energy. A great number of renewable based applications have been developed to satisfy energy demand in different fields. This paper deals with the application of artificial intelligence in photovoltaic powered AC loads. Neural fuzzy networks are applied in order to optimize the energy produced by photovoltaic generators (PVG) and successfully improving the maximum power point tracking (MPPT) control. Simulation and experimental results will be given to demonstrate the efficiency and performance of the proposed control system.	en_US
dc.language.iso	en	en_US
dc.publisher	University of Bahrain	en_US
dc.rights	Attribution-NonCommercial-ShareAlike 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-sa/4.0/	*
dc.subject	Photovoltaic generators (PVG)	en_US
dc.subject	Neural networks	en_US
dc.subject	Fuzzy logic	en_US
dc.subject	MPPT	en_US
dc.title	Neural Fuzzy Networks for Optimal MPPT Control in PV Powered AC Loads	en_US
dc.type	Article	en_US
dc.source.title	Arab Journal of Basic and Applied Sciences
dc.abbreviatedsourcetitle	AJBAS