Abstract
This paper presents the speed estimator for speed sensorless direct torque control of a three-phase induction motor based on constant voltage per frequency (V/F) control technique, using artificial neural network (ANN). The estimated stator current equation is derived and rearranged consistent with the control algorithm and ANN structure. For the speed estimation, a weight in ANN, which relates to the speed, is adjusted by using Widrow–Hoff learning rule to minimize the sum of squared errors between the measured stator current and the estimated stator current from ANN output. The consequence of using this method leads to the ability of online speed estimation and simple ANN structure. The simulation and experimental results in high- and low-speed regions have confirmed the validity of the proposed speed estimation method.
Highlights
The single biggest consumer of electricity in modern society comes from industrial and domestic electric motors
This paper presents an alternative speed estimator for speed sensorless direct torque control (DTC)-CVFC of induction motors (IMs), using
DTC-CVFC incorporated with the proposed online artificial neural network (ANN) speed estimator is simulated and experimented
Summary
The single biggest consumer of electricity in modern society comes from industrial and domestic electric motors. Three-phase induction motors (IMs) are widely used in industrial applications because they have simple structure, low cost, reliability, more efficiency, robustness and low maintenance. The two most popular high performance control methods for IM drives are field-oriented control (FOC) and direct torque control (DTC) [1,2,3]. These control algorithms can decouple control between torque and flux and offer good dynamic and steady-state torque responses
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