Abstract:

In order to manage the speed and torque of an IM drive, this research presents a direct torque control with a space vector pulse width (DTC) technique based mostly on artificial neural networks (ANN). To train the neural network, Levenberg-Marquardt back propagation (LMBP) was employed. Considering how an electric drive performs genuinely based on the speed controller's quality, it is suggested that a neural network controller be used in place of the traditional PID controllers to improve drive performance. The speed controller's neural network was developed and trained. The neural network controller has evolved and now recognises the need for a speed controller. To evaluate the controller's performance, it was applied within the feed-forward back propagation technique. A multilayer feed forward back propagation a method is employed to train the network and assess its effectiveness. Using the MATLAB/Simulink block programme, a simulation model depicting the entire neural Induction motor driving network-based direct torque control technique using svpwm is created and confirmed. The outcomes of drive for an induction motor using a space vector pulse with modulator (SVPWM) were contrasted with the outcomes of induction motor drive speed control of ANN fed DTC. Total harmonic distortion (THDTime analysis, including findings from the DTC SVPWMIM and ANNDTCIM models, as well as rising time, delay time, peak time, and overshoot have been performed.

Description:

Induction Motor (I.M), Direct Torque Control (DTC), Artificial Neural Network (ANN), Levenzberg-Marquardt back propagation (LMBP).Space Vector Pulse Width Modulation (SVPWM).