Intelligent Speed Estimation in Induction Motor Drive Control using Feed - Forward Neural Network Assisted Model Reference Adaptive System

Adjustable speed of induction motor (IM) is powered without a velocity sensor is one of the most alluring solutions with high energy efficiency, low cost, and compact size drives. The efficiency will increase enormously if these kinds of systems will come into existence through intelligent expert systems. However, the low speed of the rotor is a bit challenging in the case of an induction motor to get accurate speed estimation. The study is based on the estimation of the intensity of rotor flux using multilayered feed-forward neural network technology in a speed estimator. The proposed speed estimator replaces the conventional reference model of the Model Reference Adaptive System (MRAS). The execution of the energy with the projected estimator is tested on various operational conditions, particularly in low-speed situations. The IM speed is estimated and controlled by the Artificial Neural Network (ANN). In the proposed study 08, 20 and 02 neurons have been chosen in the input, hidden and output layers to model the ANN. The paper shows the full description of the estimation of the speed of induction Motor drive.