TDNN speed estimator applied to stator oriented IM sensorless drivers

The direct measurement of speed in induction motors is costly and requires maintenance. Thus, sensorless techniques for estimating or predicting the speed in three-phase induction motors represent a feasible and economical solution. This work considers a single time delay neural network as a speed estimator in two different strategies of stator field-oriented induction motor drive: direct current and torque control. Time delay neural network makes the estimated signal robust against noise, that is usually found in switched power systems, and against disturbances on the input signals, since the estimator is not dependent only on instantaneous values. The synchronous speed and the electromagnetic torque, which are usual quantities in field oriented drives, are the inputs of the proposed neural estimator. In order to have a robust estimator facing induction motor parameter variations, the procedure of training and validating the neural networks are conducted with three different induction motors, from simulations to the experimental tests. An embedded system is also presented, and the scheme is tested considering various speed and load torque levels with different control strategies.

Automated summary

This study proposes using a single time delay neural network as a speed estimator in order to address the high cost of direct speed measurement in induction motors. By training and validating the neural networks while considering parameter variations, the proposed method can achieve robust speed estimation and is applicable to different control strategies.