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Predictive-TOPSIS-based MPPT for PEMFC Featuring Switching Frequency Reduction

Indonesian Journal of Electical Engineering and Informatics (IJEEI) · Vol. 11 No. 3 (2023) · pp. 656–672

DOI
10.52549/ijeei.v11i3.4350
ISSN
2089-3272, 2089-3272
Published
2023-08-19
Publisher
IAES Indonesian Section
Language
en

Abstract

A maximum power point tracking (MPPT) for a proton exchange membrane fuel cell (PEMFC) using a combination of conventional finite control set model predictive control (FCS-MPC) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is proposed in this paper. The key idea is to maximize the power generation from a PEMFC while minimizing the switching frequency of the power converter. The FCS-MPC technique is formulated to track the maximum power of PEMFC highly affected by ever-changing internal parameters. Meanwhile, the TOPSIS algorithm is applied to overcome the potential weaknesses of insulated-gate bipolar transistor (IGBT), which can only withstand a lower switching frequency. In this project, all simulations were run using MATLAB software to display the output power of the PEMFC system. As a result, the proposed predictive-TOPSIS-based MPPT algorithm can track the MPP for various PEMFC parameters within 0.019 s with an excellent accuracy up to 99.11%. The proposed MPPT technique has fast-tracking of the MPP locus, excellent accuracy, and robustness to environmental changes.

Keywords

Collaboration network of the authors

31 authors · 102 co-authorship links · 35 across different organisations

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

  • Chittagong University of Engineering & Technology
  • University of Malaya
  • University of Sciences and Technology Houari Boumediene
  • Renewable Energy Development Center
  • National Polytechnic School
  • American International University-Bangladesh
  • University Mohamed Boudiaf of M'sila
  • 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
Noureddine BouarroudjRenewable Energy Development Center2
Boualam BenlahbibUniversity of Sciences and Technology Houari Boumediene2
Farid BouchafaaUniversity of Sciences and Technology Houari Boumediene1
Adil AtouiNational Polytechnic School1
Mostefa KermadiUniversity of Malaya1
Mohamed Seghir BoucheritNational Polytechnic School1
Khelifa BenmansourAffiliation unknown1
Said BarkatUniversity Mohamed Boudiaf of M'sila1
Fethi AkelRenewable Energy Development Center1
Lilik Jamilatul AwalinUniversitas Airlangga1
Aditta ChowdhuryChittagong University of Engineering & Technology1
Monirul Islam MiskatChittagong University of Engineering & Technology1
Tofael AhmedChittagong University of Engineering & Technology1
Shameem AhmadAmerican International University-Bangladesh1
Md. Rifat HazariAmerican International University-Bangladesh1
Shanti Faridah SallehAffiliation unknown1
Yonis.M.Yonis BuswigAffiliation unknown1
Azuka AffamAffiliation unknown1
Al-Khalid OthmanAffiliation unknown1
Hazrul Mohamed BasriAffiliation unknown1
Norhuzaimin JulaiAffiliation unknown1
Thameur AbdelkrimAffiliation unknown1
Abedeljalil DahbiAffiliation unknown1
Bennaceur FaresAffiliation unknown1
Abelkader LakhdariAffiliation 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://section.iaesonline.com/index.php/IJEEI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc)
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	<dc:title xml:lang="en">Predictive-TOPSIS-based MPPT for PEMFC Featuring Switching Frequency Reduction</dc:title>
	<dc:creator xml:lang="en">Fam, Jye Yun</dc:creator>
	<dc:creator xml:lang="en">Wong, Shen Yuong</dc:creator>
	<dc:creator xml:lang="en">Abdullah, Mohammad Omar</dc:creator>
	<dc:creator xml:lang="en">Lias, Kasumawati</dc:creator>
	<dc:creator xml:lang="en">Mekhilef, Saad</dc:creator>
	<dc:creator xml:lang="en">Mohamed Basri, Hazrul</dc:creator>
	<dc:subject xml:lang="en">Fuel cells</dc:subject>
	<dc:subject xml:lang="en">Maximum power point tracker</dc:subject>
	<dc:subject xml:lang="en">Predictive control</dc:subject>
	<dc:subject xml:lang="en">Power electronics</dc:subject>
	<dc:subject xml:lang="en">TOPSIS</dc:subject>
	<dc:description xml:lang="en">A maximum power point tracking (MPPT) for a proton exchange membrane fuel cell (PEMFC) using a combination of conventional finite control set model predictive control (FCS-MPC) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is proposed in this paper. The key idea is to maximize the power generation from a PEMFC while minimizing the switching frequency of the power converter. The FCS-MPC technique is formulated to track the maximum power of PEMFC highly affected by ever-changing internal parameters. Meanwhile, the TOPSIS algorithm is applied to overcome the potential weaknesses of insulated-gate bipolar transistor (IGBT), which can only withstand a lower switching frequency. In this project, all simulations were run using MATLAB software to display the output power of the PEMFC system. As a result, the proposed predictive-TOPSIS-based MPPT algorithm can track the MPP for various PEMFC parameters within 0.019 s with an excellent accuracy up to 99.11%. The proposed MPPT technique has fast-tracking of the MPP locus, excellent accuracy, and robustness to environmental changes.</dc:description>
	<dc:publisher xml:lang="en">IAES Indonesian Section</dc:publisher>
	<dc:date>2023-08-19</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://section.iaesonline.com/index.php/IJEEI/article/view/4350</dc:identifier>
	<dc:identifier>10.52549/ijeei.v11i3.4350</dc:identifier>
	<dc:source xml:lang="en">Indonesian Journal of Electrical Engineering and Informatics (IJEEI); Vol. 11 No. 3: September 2023; 656-672</dc:source>
	<dc:source>2089-3272</dc:source>
	<dc:source>2089-3272</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://section.iaesonline.com/index.php/IJEEI/article/view/4350/852</dc:relation>
</oai_dc:dc>