Soft starter of an induction motor using adaptive neuro fuzzy inference system and back propagation based feedback estimator

Document Type : Original Article

Authors

1 Associate Professor, Department of Electrical Engineering, UET, Lahore 54890, Pakistan.

2 Lecturer, Department of Electrical Engineering, UET, Lahore 54890, Pakistan.

Abstract

Abstract:
Induction motor is most widely used motor in the industry. It requires sophisticated
control of speed, inrush current and pulsations in the electromagnetic torque developed
at the starting. This paper presents the neural network based soft starter with feed back
estimator to support the on-line training of the network. Thyristor-based controller is
used whose firing angles are adjusted by adaptive neuro fuzzy inference system
(ANFIS) whose rule base was developed with the experience of the experts and off-line
simulation data. A neural network based estimator is designed using back propagation
based training algorithms to compute the electromagnetic torque, rotor angles and fluxes
fed to ANFIS to adjust the firing angle of the thyristors. Back propagation based neural
network was found to be the best among many others because this algorithm requires
very small number of neurons. The presented approach can be used with off-line
training as well as with on-line training and hence solve the problem of on-line
computation of firing angle. Estimator developed was compared in results with the
DSP- based estimator and results are shown.

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