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State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems - a review

Seyedmahmoudian, M., Horan, B., Kok Soon, T., Rahmani, R., Maung Than Oo, A., Mekhilef, S. and Stojcevski, A. 2016, State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems - a review, Renewable and sustainable energy reviews, vol. 64, pp. 435-455, doi: 10.1016/j.rser.2016.06.053.

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Title State of the art artificial intelligence-based MPPT techniques for mitigating partial shading effects on PV systems - a review
Author(s) Seyedmahmoudian, M.
Horan, B.ORCID iD for Horan, B. orcid.org/0000-0002-6723-259X
Kok Soon, T.
Rahmani, R.
Maung Than Oo, A.
Mekhilef, S.
Stojcevski, A.
Journal name Renewable and sustainable energy reviews
Volume number 64
Start page 435
End page 455
Total pages 21
Publisher Elsevier
Place of publication Oxford, Eng.
Publication date 2016-10
ISSN 1364-0321
1879-0690
Keyword(s) maximum power point tracking
photovoltaic systems
partial shading
artificial intelligence
soft computing
Science & Technology
Technology
GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY
Energy & Fuels
Science & Technology - Other Topics
Phtovoltaic systems
POWER-POINT TRACKING
INCREMENTAL CONDUCTANCE MPPT
PARTIALLY SHADED CONDITIONS
ANT COLONY OPTIMIZATION
ALONE PHOTOVOLTAIC SYSTEMS
FUZZY-LOGIC-CONTROLLER
SWARM OPTIMIZATION
NEURAL-NETWORK
EFFICIENCY OPTIMIZATION
HARDWARE IMPLEMENTATION
Summary Given the considerable recent attention to distributed power generation and interest in sustainable energy, the integration of photovoltaic (PV) systems to grid-connected or isolated microgrids has become widespread. In order to maximize power output of PV system extensive research into control strategies for maximum power point tracking (MPPT) methods has been conducted. According to the robust, reliable, and fast performance of artificial intelligence-based MPPT methods, these approaches have been applied recently to various systems under different conditions. Given the diversity of recent advances to MPPT approaches a review focusing on the performance and reliability of these methods under diverse conditions is required. This paper reviews AI-based techniques proven to be effective and feasible to implement and very common in literature for MPPT, including their limitations and advantages. In order to support researchers in application of the reviewed techniques this study is not limited to reviewing the performance of recently adopted methods, rather discusses the background theory, application to MPPT systems, and important references relating to each method. It is envisioned that this review can be a valuable resource for researchers and engineers working with PV-based power systems to be able to access the basic theory behind each method, select the appropriate method according to project requirements, and implement MPPT systems to fulfill project objectives.
Language eng
DOI 10.1016/j.rser.2016.06.053
Field of Research 090607 Power and Energy Systems Engineering (excl Renewable Power)
090608 Renewable Power and Energy Systems Engineering (excl Solar Cells)
090699 Electrical and Electronic Engineering not elsewhere classified
Socio Economic Objective 850604 Energy Transmission and Distribution (excl. Hydrogen)
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30084792

Document type: Journal Article
Collection: School of Engineering
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Created: Tue, 13 Sep 2016, 11:33:28 EST

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