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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: | |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Maejo University
2014-03-01
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| Colecção: | Maejo International Journal of Science and Technology |
| Assuntos: | |
| Acesso em linha: | http://www.mijst.mju.ac.th/vol8/58-74.pdf |
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| Resumo: | 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. |
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| ISSN: | 1905-7873 1905-7873 |