Application of Artificial Neural Network for Analysis of Self-Excited Induction Generator

It is observed that conventional techniques to analyse the steady state analysis of Self-Excited Induction Generator (SEIG) involve cumbersome mathematical procedures. In this paper an Artificial Intelligence (AI) technique has been used to analyse the behaviour of Self-Excited Induction Generator,...

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Detalles Bibliográficos
Autores principales: Khela, Raja Singh, Bansal, Raj Kumar, Sandhu, K. S., Goel, Ashok Kumar
Formato: Articulo
Lenguaje:Inglés
Publicado: 2006
Materias:
Ciencias Informáticas
Connectionism and neural nets
ARTIFICIAL INTELLIGENCE
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/9541
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct06-3.pdf
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-9541
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Connectionism and neural nets
ARTIFICIAL INTELLIGENCE
spellingShingle Ciencias Informáticas
Connectionism and neural nets
ARTIFICIAL INTELLIGENCE
Khela, Raja Singh
Bansal, Raj Kumar
Sandhu, K. S.
Goel, Ashok Kumar
Application of Artificial Neural Network for Analysis of Self-Excited Induction Generator
topic_facet Ciencias Informáticas
Connectionism and neural nets
ARTIFICIAL INTELLIGENCE
description It is observed that conventional techniques to analyse the steady state analysis of Self-Excited Induction Generator (SEIG) involve cumbersome mathematical procedures. In this paper an Artificial Intelligence (AI) technique has been used to analyse the behaviour of Self-Excited Induction Generator, which does not require rigorous modelling as required in conventional techniques. Proposed Artificial Neural Network (ANN) model has been implemented to predict the effect of speed, capacitance and load on generated voltage and frequency of SEIG. Experimental data is used for the training of ANN. Results obtained from the trained ANN are found to be in close agreement with the experimental results.
format Articulo
Articulo
author Khela, Raja Singh
Bansal, Raj Kumar
Sandhu, K. S.
Goel, Ashok Kumar
author_facet Khela, Raja Singh
Bansal, Raj Kumar
Sandhu, K. S.
Goel, Ashok Kumar
author_sort Khela, Raja Singh
title Application of Artificial Neural Network for Analysis of Self-Excited Induction Generator
title_short Application of Artificial Neural Network for Analysis of Self-Excited Induction Generator
title_full Application of Artificial Neural Network for Analysis of Self-Excited Induction Generator
title_fullStr Application of Artificial Neural Network for Analysis of Self-Excited Induction Generator
title_full_unstemmed Application of Artificial Neural Network for Analysis of Self-Excited Induction Generator
title_sort application of artificial neural network for analysis of self-excited induction generator
publishDate 2006
url http://sedici.unlp.edu.ar/handle/10915/9541
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct06-3.pdf
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AT bansalrajkumar applicationofartificialneuralnetworkforanalysisofselfexcitedinductiongenerator
AT sandhuks applicationofartificialneuralnetworkforanalysisofselfexcitedinductiongenerator
AT goelashokkumar applicationofartificialneuralnetworkforanalysisofselfexcitedinductiongenerator
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