Speed Control of Induction Motor Drive Using Artificial Neural Networks-Levenberg-Marquardt Backpropogation Algorithm

摘要

This paper affords an artificial neural network (ANN) primarily based space vector pulse width modulated direct torque control (DTC) scheme to control speed and torque of IM drive. The Levenberg-Marquardt back propagation (LMBP) technique has been used to train the neural network. A neural network controller is proposed to replace the conventional PID controllers to enhance the drive's performance since the performance of an electric drive genuinely relies upon on the excellent of a speed controller. The neural network controller was trained and realizes for a speed controller. The neural community controller changed into educated and realizes for a speed controller. The controller become applied within the feed-forward back propagation algorithm to test its performance. the network is trained the use of multi layer feed forward back propagation algorithm to check its performance. A simulation model representing the complete neural network based direct torque control scheme of induction motor drive using svpwm is developed and verified using MATLAB/Simulink block program. The results of ANN fed DTC based speed control of induction motor drive compared with the results of space vector pulse with modulator (SVPWM) controlled induction motor (I.M) drive. Time analysis (rise time, delay time, peak time and over shoot), total harmonic distortion (THD) of both (DTC SVPWMIM and ANNDTCIM) models has been done and results are compared.