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Wind Farm Management using Artificial Intelligent Techniques

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 7 No. 3 (2017)

DOI
10.11591/ijece.v7i3.pp1133-1144
ISSN
2088-8708, 2088-8708
Published
2017-06-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

This paper presents a comparative study between genetic algorithm and particle swarm optimization methods to determine the optimal proportional–integral (PI) controller parameters for a wind farm management algorithm. This study primarily aims to develop a rapid and stable system by tuning the PI controller, thus providing excellent monitoring for a wind farm system. The wind farm management system supervises the active and reactive power of the wind farm by sending references to each wind generator. This management system ensures that all wind generators achieve their required references. Furthermore, the entire management is included in the normal controlling power set points of the wind farm as designed by a central control system. The performance management of this study is tested through MATLAB/Simulink simulation results for the wind farm based on three doublyfed induction generators

Collaboration network of the authors

18 authors · 51 co-authorship links · 36 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 Sciences and Technology Houari Boumediene
  • University of Malaya
  • Renewable Energy Development Center
  • National Polytechnic School
  • American International University-Bangladesh
  • Polytechnic School of Algiers
  • 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
Djamel BoukhetalaPolytechnic School of Algiers2
Farès BoudjemaAffiliation unknown2
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

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>Wind Farm Management using Artificial Intelligent Techniques</dc:title><dc:creator>Benlahbib, Boualam</dc:creator><dc:creator>Bouchafaa, Farid</dc:creator><dc:creator>Mekhilef, Saad</dc:creator><dc:creator>Bouarroudj, Noureddine</dc:creator><dc:description>&lt;jats:p&gt;This paper presents a comparative study between genetic algorithm and particle swarm optimization methods to determine the optimal proportional–integral (PI) controller parameters for a wind farm management algorithm. This study primarily aims to develop a rapid and stable system by tuning the PI controller, thus providing excellent monitoring for a wind farm system. The wind farm management system supervises the active and reactive power of the wind farm by sending references to each wind generator. This management system ensures that all wind generators achieve their required references. Furthermore, the entire management is included in the normal controlling power set points of the wind farm as designed by a central control system. The performance management of this study is tested through MATLAB/Simulink simulation results for the wind farm based on three doublyfed induction generators&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2017-06-01</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.v7i3.pp1133-1144</dc:identifier><dc:identifier>10.11591/ijece.v7i3.pp1133-1144</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 7 No 3 (2017); 1133</dc:source><dc:source>2088-8708</dc:source><dc:source>2088-8708</dc:source><dc:relation>http://ijece.iaescore.com/index.php/IJECE/article/viewFile/7472/6547</dc:relation><dc:rights>http://creativecommons.org/licenses/by-nc/4.0</dc:rights></oai_dc:dc>