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A New Hybrid Artificial Neural Network Based Control of Doubly Fed Induction Generator

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 5 No. 3 (2015)

DOI
10.11591/ijece.v5i3.pp379-390
ISSN
2088-8708, 2088-8708
Published
2015-06-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-nc/4.0

Abstract

In this paper, Hybrid Artificial Neural Network (ANN) with Proportional Integral (PI) control technique has been developed for Doubly Fed Induction Generator (DFIG) based wind energy generation system and the performance of the system is compared with NN and PI control techniques. With the increasing use of wind power generation, it is required to instigate the dynamic performance analysis of Doubly Fed Induction Generator under various operating conditions. In this paper, three control techniques have been proposed, the first one is using PI controller, the second one is ANN control, and the third one is based on combination of ANN and PI. The performance of the proposed control techniques is demonstrated through the results, determined by using MATLab/Simulink. From the results it is observed that the dynamic performance of the DFIG is improved with the Hybrid control technique.

Collaboration network of the authors

4 authors · 4 co-authorship links · 1 across different organisations

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  • Annamalai University
  • Koneru Lakshmaiah Education Foundation
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Kishore ParapellyAnnamalai University1
C. MahalakshmiAnnamalai 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 27 Okt 2026.
Metadata as oai_dc, converted from Crossref
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