<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>

COPY SCRIPT

For further information contact us at helpdesk@openaire.eu

Intelligent maximum power point tracking for PV system using Hopfield neural network optimized fuzzy logic controller

descriptionPublicationkeyboard_double_arrow_right Article , Journal 01 Aug 2012Publisher:Elsevier BVJournal:Energy and Buildings, volume 51, pages 29-38 (issn: 0378-7788,

Authors: Subiyanto Subiyanto; Azah Mohamed; Mahammad A. Hannan;

doi: 10.1016/j.enbuild.2012.04.012

Intelligent maximum power point tracking for PV system using Hopfield neural network optimized fuzzy logic controller

- Summary
- Metrics

Abstract

Abstract This paper presents a new method for maximum power point tracking of photovoltaic (PV) energy harvesting system by using the Hopfield neural network (HNN) optimized fuzzy logic controller (FLC). In the proposed method, HNN is utilized to automatically tune the FLC membership functions instead of adopting the trial-and-error approach. A complete simulation model of a PV system using the MATLAB/Simulink software is developed to validate the HNN optimized FLC. A hardware prototype of the PV maximum power point tracking controller was also implemented using the dSPACE DS1104 controller. Simulation and experimental results show the performance and effectiveness of the HNN optimized FLC. It is proven that the proposed HNN optimized FLC can provide accurate tracking of the PV maximum power point and improve the efficiency of PV systems.

Related Organizations

National University of Malaysia
Malaysia
Universiti Malaysia Terengganu
Malaysia
National University of Malaysia
Malaysia

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	110
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 1%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 1%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 1%