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Adaptive virtual inertia controller based on machine learning for superconducting magnetic energy storage for dynamic response enhanced

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 13 No. 4 (2023)

DOI
10.11591/ijece.v13i4.pp3651-3659
ISSN
2722-2578, 2088-8708
Published
2023-08-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The goal of this paper was to create an adaptive virtual inertia controller (VIC) for superconducting magnetic energy storage (SMES). An adaptive virtual inertia controller is designed using an extreme learning machine (ELM). The test system is a 25-bus interconnected Java Indonesian power grid. Time domain simulation is used to evaluate the effectiveness of the proposed controller method. To simulate the case study, the MATLAB/Simulink environment is used. According to the simulation results, an extreme learning machine can be used to make the virtual inertia controller adaptable to system variation. It has also been discovered that designing virtual inertia based on an extreme learning machine not only makes the VIC adaptive to any change in the system but also provides better dynamics performance when compared to other scenarios (the overshoot value of adaptive VIC is less than -5×10-5).

Collaboration network of the authors

19 authors · 53 co-authorship links · 31 across different organisations

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

  • Universitas Airlangga
  • Institut Teknologi Sepuluh Nopember
  • Universitas Pertamina
  • Liverpool John Moores University
  • Telkom University
  • Politeknik Siber dan Sandi Negara
  • SRM Institute of Science and Technology
  • Other organisations
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Karl O. JonesLiverpool John Moores University2
Teguh Aryo NugrohoUniversitas Pertamina2
Herri TrilaksanaUniversitas Airlangga2
Tahta AmrillahUniversitas Airlangga2
Prisma MegantoroUniversitas Airlangga2
Brahmantya Aji PramuditaTelkom University1
Dimas Febriyan PriambodoPoliteknik Siber dan Sandi Negara1
Hafidz Faqih Aldi KusumaUniversitas Airlangga1
Lilik Jamilatul AwalinUniversitas Airlangga1
P. VigneshwaranSRM Institute of Science and Technology1
Adi SoeprijantoInstitut Teknologi Sepuluh Nopember1
Bima MustaqimInstitut Teknologi Sepuluh Nopember1
Fauzan NusyuraInstitut Teknologi Telkom Surabaya1
Ardyono PriyadiInstitut Teknologi Sepuluh Nopember1
Imam AbadiInstitut Teknologi Sepuluh Nopember1

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.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Adaptive virtual inertia controller based on machine learning for superconducting magnetic energy storage for dynamic response enhanced</dc:title><dc:creator>Setiadi, Herlambang</dc:creator><dc:creator>Abdillah, Muhammad</dc:creator><dc:creator>Afif, Yusrizal</dc:creator><dc:creator>Delfianti, Rezi</dc:creator><dc:description>&lt;jats:p&gt;The goal of this paper was to create an adaptive virtual inertia controller (VIC) for superconducting magnetic energy storage (SMES). An adaptive virtual inertia controller is designed using an extreme learning machine (ELM). The test system is a 25-bus interconnected Java Indonesian power grid. Time domain simulation is used to evaluate the effectiveness of the proposed controller method. To simulate the case study, the MATLAB/Simulink environment is used. According to the simulation results, an extreme learning machine can be used to make the virtual inertia controller adaptable to system variation. It has also been discovered that designing virtual inertia based on an extreme learning machine not only makes the VIC adaptive to any change in the system but also provides better dynamics performance when compared to other scenarios (the overshoot value of adaptive VIC is less than -5×10-5).&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2023-08-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v13i4.pp3651-3659</dc:identifier><dc:identifier>10.11591/ijece.v13i4.pp3651-3659</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 13 No 4 (2023); 3651</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>https://ijece.iaescore.com/index.php/IJECE/article/viewFile/29371/16649</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>