MPPT-based artificial intelligence techniques for photovoltaic systems and its implementation into field programmable gate array chips: Review of current status and future perspectives
Adel Mellit and
Soteris A. Kalogirou
Energy, 2014, vol. 70, issue C, 1-21
Abstract:
In this paper, the applications of artificial intelligence-based methods for tracking the maximum power point have been reviewed and analysed. The reviewed methods are based upon neural networks, fuzzy logic, evolutionary algorithms, which include genetic algorithms, particle swarm optimization, ant colony optimization, and other hybrid methods. Rapid advances in programmable logic devices (PLDs) including field programmable gate arrays (FPGAs) give good opportunities to integrate efficiently such techniques for real time applications. An attempt is made to highlight the future trends and challenges in the development of embedded intelligent digital maximum power point tracking (MPPT) controllers into FPGA chip. Special attention is also given to the cost, complexity of implementation, efficiency, and possible practical realization. We believe that this review provides valuable information for engineers, designers and scientist working in this area and show future trends in the development of embedded intelligent techniques for renewable energy systems.
Keywords: Photovoltaic systems; Maximum power point tracking; Artificial intelligence; Implementation; Programmable logic devices; Field programmable gate arrays chip; Real-time applications (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations: View citations in EconPapers (30)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544214003661
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:70:y:2014:i:c:p:1-21
DOI: 10.1016/j.energy.2014.03.102
Access Statistics for this article
Energy is currently edited by Henrik Lund and Mark J. Kaiser
More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu (repec@elsevier.com).