Influence of the training set selection on the performance of the neural network state variables estimators in the induction motor

Abstract

In the paper three neural networks state variables estimators of the induction motor are considered, which recreate rotor angular speed, rotor flux and stator current components in the rotor flux reference frame. Input variables for the neural estimators are the components of stator current and voltage to allow for sensor less control of induction motor drive. Performance of the estimators is compared for the networks trained using static, dynamic and mixed sets of data. Intention of the analysis is to find the best way the training data are obtained that assures possibly high accuracy of the estimators.