An adaptive radial basis function neural network (RBFNN) control of energy storage system for output tracking of a permanent magnet wind generator

The converters of a permanent magnet synchronous generator have to be properly controlled to achieve maximum transfer of energy from wind. To achiev e this goal, this article employs an energy storage device consisting of an energy capacitor interfaced through a vol...

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Autor principal: Abu H. M. A. Rahim
Formato: Artigo
Idioma:Inglês
Publicado em: Maejo University 2014-03-01
Colecção:Maejo International Journal of Science and Technology
Assuntos:
adaptive control
energy storage control
radial basis function neural network
permanent magnet synchronous generator
wind turbine
Acesso em linha:http://www.mijst.mju.ac.th/vol8/58-74.pdf
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id oai:doaj.org-article:c0fcc250893b4d00ab3e1c9b3efb1439
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spelling oai:doaj.org-article:c0fcc250893b4d00ab3e1c9b3efb14392022-04-13T00:31:08ZengMaejo UniversityMaejo International Journal of Science and Technology1905-78731905-78732014-03-01801587410.14456/mijst.2014.6An adaptive radial basis function neural network (RBFNN) control of energy storage system for output tracking of a permanent magnet wind generatorAbu H. M. A. Rahim0Department of Electrical Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaThe converters of a permanent magnet synchronous generator have to be properly controlled to achieve maximum transfer of energy from wind. To achiev e this goal, this article employs an energy storage device consisting of an energy capacitor interfaced through a voltage source converter which is operated through a smart adaptive radial basis function neural network (RBFNN) controller. The proposed adaptive strategy employs online neural network training as opposed to conventional procedure requiring offline training of a large data-set. The RBFNN controller was tested for various contingencies in the wind generator system. Th e adaptive online controller is observed to provide excellent damping profile following low grid voltage conditions as well as for other large disturbances. The controlled converter DC capacitor voltage helps maintain a smooth flow of real and reactive power in the system.http://www.mijst.mju.ac.th/vol8/58-74.pdfadaptive controlenergy storage controlradial basis function neural networkpermanent magnet synchronous generatorwind turbine
institution DOAJ
collection Directory of Open Access Journals
language Inglês
format Artigo
author Abu H. M. A. Rahim
spellingShingle Abu H. M. A. Rahim
An adaptive radial basis function neural network (RBFNN) control of energy storage system for output tracking of a permanent magnet wind generator
Maejo International Journal of Science and Technology
adaptive control
energy storage control
radial basis function neural network
permanent magnet synchronous generator
wind turbine
author_facet Abu H. M. A. Rahim
author_sort Abu H. M. A. Rahim
title An adaptive radial basis function neural network (RBFNN) control of energy storage system for output tracking of a permanent magnet wind generator
title_short An adaptive radial basis function neural network (RBFNN) control of energy storage system for output tracking of a permanent magnet wind generator
title_full An adaptive radial basis function neural network (RBFNN) control of energy storage system for output tracking of a permanent magnet wind generator
title_fullStr An adaptive radial basis function neural network (RBFNN) control of energy storage system for output tracking of a permanent magnet wind generator
title_full_unstemmed An adaptive radial basis function neural network (RBFNN) control of energy storage system for output tracking of a permanent magnet wind generator
title_sort adaptive radial basis function neural network (rbfnn) control of energy storage system for output tracking of a permanent magnet wind generator
publisher Maejo University
series Maejo International Journal of Science and Technology
issn 1905-7873
1905-7873
publishDate 2014-03-01
description The converters of a permanent magnet synchronous generator have to be properly controlled to achieve maximum transfer of energy from wind. To achiev e this goal, this article employs an energy storage device consisting of an energy capacitor interfaced through a voltage source converter which is operated through a smart adaptive radial basis function neural network (RBFNN) controller. The proposed adaptive strategy employs online neural network training as opposed to conventional procedure requiring offline training of a large data-set. The RBFNN controller was tested for various contingencies in the wind generator system. Th e adaptive online controller is observed to provide excellent damping profile following low grid voltage conditions as well as for other large disturbances. The controlled converter DC capacitor voltage helps maintain a smooth flow of real and reactive power in the system.
topic adaptive control
energy storage control
radial basis function neural network
permanent magnet synchronous generator
wind turbine
url http://www.mijst.mju.ac.th/vol8/58-74.pdf
_version_ 1735604332675465216

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