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Sinopsis
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.
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Reseña del editor
Starting inrush current and pulsations in the inducedtorque affect the performance of an induction motor.Artificial neural networks (ANNs) and adaptive neurofuzzy inference system (ANFIS) can enhance theperformance of the motor by making a control systemwhich would provide smooth starting to inductionmotor. Dynamic model of induction machine indifferent frames of reference was implemented usingMatlab Simulink. Feed forward back propagation basedand radial basis neural networks were trained, withdata obtained using simulations, to estimatedifferent parameters required by ANFIS to adjustfiring angle of back-to-back connected pairs ofthyristors in AC voltage controller. Inrush currentand pulsations in torque were reduced significantly.Radial basis and feed forward neural networks werecompared for off-line and on-line training, trainingtime, memory required for implementations, number ofneurons, computational procedures and algorithms,reliability of the system and most important cost ofimplementation. Artificial neural networks andAdaptive neuro fuzzy inference system were developedusing tool boxes in Matlab Simulink.
Biografía del autor
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.
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Soft Starting of Induction Motors
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LAP Lambert Academic Publishing Okt 2016, 2016
ISBN 10: 3844327762
ISBN 13: 9783844327762
Nuevo
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 104 pp. Englisch. Nº de ref. del artículo: 9783844327762
Cantidad disponible: 2 disponibles
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Soft Starting of Induction Motors
Publicado por
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3844327762
ISBN 13: 9783844327762
Nuevo
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Librería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kashif Syed Abdul RahmanSyed 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 Depa. Nº de ref. del artículo: 5473176
Cantidad disponible: Más de 20 disponibles
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Soft Starting of Induction Motors : using Microcontrollers and ANN Model
Publicado por
LAP Lambert Academic Publishing, 2011
ISBN 10: 3844327762
ISBN 13: 9783844327762
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9783844327762
Cantidad disponible: 1 disponibles
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Soft Starting of Induction Motors
Publicado por
LAP Lambert Academic Publishing Okt 2016, 2016
ISBN 10: 3844327762
ISBN 13: 9783844327762
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
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Taschenbuch. Condición: Neu. Neuware -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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 104 pp. Englisch. Nº de ref. del artículo: 9783844327762
Cantidad disponible: 2 disponibles
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Soft Starting of Induction Motors: using Microcontrollers and ANN Model
Publicado por
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3844327762
ISBN 13: 9783844327762
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Paperback. Condición: Like New. Like New. book. Nº de ref. del artículo: ERICA75838443277626
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