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Comparative analysis of recent metaheuristic algorithms for maximum power point tracking of solar photovoltaic systems under partial shading conditions

International Journal of Applied Power Engineering (IJAPE) · Vol. 12 No. 2 (2023) · pp. 196–217

DOI
10.11591/ijape.v12.i2.pp196-217
ISSN
2722-2624, 2252-8792
Published
2023-06-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The photovoltaic (PV) system comprises one or more solar panels, a converter/inverter, controllers, and other mechanical and electrical elements that utilize the generated electrical energy by the PV modules. The PV systems are ranged from small roofs or transportable units to massive electric utility plants. The maximum power point tracking (MPPT) controller has been used in PV systems to get the maximum power available. In addition, the MPPT controller is much essential for PV systems to protect the battery devices or direct loads from the power fluctuations received from solar PV panels. There are several MPPT control mechanisms available right now. The most common and commonly applied approaches under constant irradiance are perturb and observe (P&O) and incremental conductance (INC). But such methods show variations in the maximum power point. In this sense, this paper analyses and utilizes two recent metaheuristic algorithms called artificial rabbit optimization (ARO) and the most valuable player (MVP) algorithm for MPPT applications. The performance comparisons are made with the most preferred traditional algorithms, such as P&O and INC. Based on the result obtained, this study recommends that ARO perform better in standard testing conditions than all the other algorithms, but in partially shaded conditions, the MVP algorithm performs better in terms of efficiency and tracking speed.

Keywords

Collaboration network of the authors

28 authors · 75 co-authorship links · 34 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Chouaib Doukkali University
  • Universitas Negeri Surabaya
  • Université Hassan 1er
  • Al al-Bayt University
  • Nigerian Defence Academy
  • Jordan University of Science and Technology
  • Amman Arab University
  • Other organisations
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Widi AribowoUniversitas Negeri Surabaya4
Aliyu SaboNigerian Defence Academy3
Toufik MziliChouaib Doukkali University2
Diego OlivaAffiliation unknown2
M. PremkumarGMR Institute of Technology2
G. Saikiran ReddyAffiliation unknown2
Ahmed AbatalUniversité Hassan 1er1
Mourad MziliChouaib Doukkali University1
Zakaria BenlaliaChouaib Doukkali University1
Hajar KhalloukiUniversité Hassan 1er1
Mohammed El Kaim BillahChouaib Doukkali University1
Mutasem K. AlsmadiImam Abdulrahman Bin Faisal University1
Ghaith M. JaradatAmman Arab University1
Mohamad I. Al‐WidyanJordan University of Science and Technology1
Diego A. OlivaUniversidad de Guadalajara1
Abeer A. AljohaniTaibah University1
Abeer AljohaniAffiliation unknown1
Ayusta Lukita WardaniUniversitas Negeri Surabaya1
Aditya PrapancaAffiliation unknown1
Reza RahmadianAffiliation unknown1
Mahendra WidyartonoAffiliation unknown1
Chandrasekaran KumarAffiliation unknown1
Ravichandran SowmyaAffiliation unknown1
Kanagarathinam KarthickAffiliation unknown1
Rayichandran SowmyaAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ijape.iaescore.com/index.php/IJAPE/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Comparative analysis of recent metaheuristic algorithms for maximum power point tracking of solar photovoltaic systems under partial shading conditions</dc:title>
	<dc:creator>Ravi, Suraj</dc:creator>
	<dc:creator>Premkumar, Manoharan</dc:creator>
	<dc:creator>Abualigah, Laith</dc:creator>
	<dc:subject xml:lang="en-US">boost converter; metaheuristic algorithm; MPPT algorithms; partial shading conditions</dc:subject>
	<dc:description xml:lang="en-US">The photovoltaic (PV) system comprises one or more solar panels, a converter/inverter, controllers, and other mechanical and electrical elements that utilize the generated electrical energy by the PV modules. The PV systems are ranged from small roofs or transportable units to massive electric utility plants. The maximum power point tracking (MPPT) controller has been used in PV systems to get the maximum power available. In addition, the MPPT controller is much essential for PV systems to protect the battery devices or direct loads from the power fluctuations received from solar PV panels. There are several MPPT control mechanisms available right now. The most common and commonly applied approaches under constant irradiance are perturb and observe (P&amp;amp;O) and incremental conductance (INC). But such methods show variations in the maximum power point. In this sense, this paper analyses and utilizes two recent metaheuristic algorithms called artificial rabbit optimization (ARO) and the most valuable player (MVP) algorithm for MPPT applications. The performance comparisons are made with the most preferred traditional algorithms, such as P&amp;amp;O and INC. Based on the result obtained, this study recommends that ARO perform better in standard testing conditions than all the other algorithms, but in partially shaded conditions, the MVP algorithm performs better in terms of efficiency and tracking speed.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2023-06-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijape.iaescore.com/index.php/IJAPE/article/view/20592</dc:identifier>
	<dc:identifier>10.11591/ijape.v12.i2.pp196-217</dc:identifier>
	<dc:source xml:lang="en-US">International Journal of Applied Power Engineering (IJAPE); Vol 12, No 2: June 2023; 196-217</dc:source>
	<dc:source>2722-2624</dc:source>
	<dc:source>2252-8792</dc:source>
	<dc:source>10.11591/ijape.v12.i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijape.iaescore.com/index.php/IJAPE/article/view/20592/13062</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Suraj R, Premkumar M</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>