Abstract
Two approaches to speed estimation of an induction motor in the drive system, utilizing only easy measurable electrical signals, are presented, discussed and compared. One is based on phenomenological model of the motor and least squares solution of an overdetermined set of linear equations. Another utilizes nonlinear system modeling via neural network. These two models are complementarily treated in the paper. The phenomenological model is simple and easily interpretable, but it is very sensitive to parameter changes. The neural model requires that input variables are preprocessed. We demonstrate that the input variables selection, sampling time and neural architecture are interrelated and that time instances of sinusoidal signals such as stator voltages and currents, without preprocessing, are not convenient for speed estimation. It is proved that for the most commonly used tapped delay neural architecture, the speed estimation cannot be improved above certain level of accuracy through sampling time selection, or enlarging number of delays of the input signals. Preprocessing of the input variables may change the situation. The information obtained from phenomenological model is used to select and preprocess input variables for the neural model. Simulation examples demonstrating both approaches and very good efficiency and robustness of the neural model are included.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call