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, �berseering 33, 22297 Hamburg 104 pp. Englisch.

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Detalles bibliogr�ficos

T�tulo: Soft Starting of Induction Motors
Editor: LAP Lambert Academic Publishing Okt 2016
Fecha de publicaci�n: 2016
Idioma: Ingl�s
ISBN 10: 3844327762
ISBN 13: 9783844327762
Encuadernaci�n: Taschenbuch
Estado: Neu

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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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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.

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