Articles liés à Soft Starting of Induction Motors: using Microcontrollers...

Soft Starting of Induction Motors: using Microcontrollers and ANN Model - Couverture souple

Kashif, Syed Abdul Rahman; Asghar Saqib, Muhammad

 
9783844327762: Soft Starting of Induction Motors: using Microcontrollers and ANN Model
Couverture souple
ISBN 10 : 3844327762 ISBN 13 : 9783844327762
Editeur : LAP LAMBERT Academic Publishing, 2011

Synopsis

Starting inrush current and pulsations in the induced torque affect the performance of an induction motor. Artificial neural networks (ANNs) and adaptive neuro fuzzy inference system (ANFIS) can enhance the performance of the motor by making a control system which would provide smooth starting to induction motor. Dynamic model of induction machine in different frames of reference was implemented using Matlab Simulink. Feed forward back propagation based and radial basis neural networks were trained, with data obtained using simulations, to estimate different parameters required by ANFIS to adjust firing angle of back-to-back connected pairs of thyristors in AC voltage controller. Inrush current and pulsations in torque were reduced significantly. Radial basis and feed forward neural networks were compared for off-line and on-line training, training time, memory required for implementations, number of neurons, computational procedures and algorithms, reliability of the system and most important cost of implementation. Artificial neural networks and Adaptive neuro fuzzy inference system were developed using tool boxes in Matlab Simulink.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Starting inrush current and pulsations in the induced torque affect the performance of an induction motor. Artificial neural networks (ANNs) and adaptive neuro fuzzy inference system (ANFIS) can enhance the performance of the motor by making a control system which would provide smooth starting to induction motor. Dynamic model of induction machine in different frames of reference was implemented using Matlab Simulink. Feed forward back propagation based and radial basis neural networks were trained, with data obtained using simulations, to estimate different parameters required by ANFIS to adjust firing angle of back-to-back connected pairs of thyristors in AC voltage controller. Inrush current and pulsations in torque were reduced significantly. Radial basis and feed forward neural networks were compared for off-line and on-line training, training time, memory required for implementations, number of neurons, computational procedures and algorithms, reliability of the system and most important cost of implementation. Artificial neural networks and Adaptive neuro fuzzy inference system were developed using tool boxes in Matlab Simulink.

Biographie de l'auteur

Syed Abdul Rahman Kashif did his B.Sc. and M.Sc. in ElectricalPower, in 2005 and 2008 respectively, from University ofEngineering and Technology, Lahore. He is now pursuing his PhDand is a Lecturer in the Department of Electrical Engineeringat UET, Lahore. His major research area is Applications ofArtificial Intelligence in Electrical Drives.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2011
Langue
anglais
ISBN 10 
3844327762
ISBN 13 
9783844327762
Reliure
Broché
Nombre de pages
104
Coordonnées du fabricant
non disponible
Personne responsable
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