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Swarm algorithms in dynamic optimization problem of reactive power compensation units control

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 9 No. 5 (2019)

DOI
10.11591/ijece.v9i5.pp3967-3974
ISSN
2722-2578, 2088-8708
Published
2019-10-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Optimization of a power supply system is one of the main directions in power engineering research. The reactive power compensation reduces active power losses in transmission lines. In general, researches devoted to allocation and control of the compensation units consider this issue as a static optimization problem. However, it is dynamic and stochastic optimization problem that requires a real-time solution. To solve the dynamic optimization NP-hard problem, it is advisable to use Swarm Intelligence. This research deals with the problem of the compensation units power control as a dynamic optimization problem, considering the possible stochastic failures of the compensation units. The Particle Swarm Optimization and the Bees Algorithm were applied to solve it to compare the effectiveness of these algorithms in the dynamic optimization of a power supply system.

Collaboration network of the authors

14 authors · 23 co-authorship links · 3 across different organisations

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

  • Novosibirsk State Technical University
  • Surgut State University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
E. S. TretiakovaAffiliation unknown4
D. V. AntonenkovAffiliation unknown3
Arseniy V. SemenovNovosibirsk State Technical University2
Dmitry A. PavlyuchenkoNovosibirsk State Technical University1
Javod S. AhyoevNovosibirsk State Technical University1
Dmitrij Olegovich KrjukovNovosibirsk State Technical University1
Pavel V. MatreninAffiliation unknown1
Lola Sh. AtabaevaNovosibirsk State Technical University1
D. O. KriukovAffiliation unknown1
Dmitriy M. IvanovNovosibirsk State Technical University1
Gennady V. IvanovSurgut State University1

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 1 Nov 2026.
Metadata as oai_dc, converted from Crossref
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