Online Speed Estimation Using Artificial Neural Network for Speed Sensorless Direct Torque Control of Induction Motor based on Constant V/F Control Technique

Narongrit Pimkumwong and Ming-Shyan Wang
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Narongrit Pimkumwong: Department of Electrical Engineering, Southern Taiwan University of Science and Technology, No.1, Nan-Tai Street, Yung Kang District, Tainan City 71005, Taiwan
Ming-Shyan Wang: Department of Electrical Engineering, Southern Taiwan University of Science and Technology, No.1, Nan-Tai Street, Yung Kang District, Tainan City 71005, Taiwan

Energies, 2018, vol. 11, issue 8, 1-14

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.

Keywords: speed estimation; artificial neural network; direct torque control; induction motor drives (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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