Online Rotor and Stator Resistance Estimation Based on Artificial Neural Network Applied in Sensorless Induction Motor Drive

Tuan Pham Van, Dung Vo Tien, Zbigniew Leonowicz, Michal Jasinski, Tomasz Sikorski and Prasun Chakrabarti
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Tuan Pham Van: Faculty of Electrical Engineering, Vinh University of Technology Education, 117 Nguyen Viet Xuan Street, Vinh City 890000, Vietnam
Dung Vo Tien: Faculty of Electrical Engineering, Vinh University of Technology Education, 117 Nguyen Viet Xuan Street, Vinh City 890000, Vietnam
Zbigniew Leonowicz: Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
Michal Jasinski: Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
Tomasz Sikorski: Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
Prasun Chakrabarti: Department of Computer Science and Engineering, Techno India NJR Institute of Technology Udaipur, Rajasthan 313003, India

Energies, 2020, vol. 13, issue 18, 1-16

Abstract: This paper presents a new approach method for online rotor and stator resistance estimation of induction motors using artificial neural networks for the sensorless drive. In this method, the rotor resistance is estimated by a feed-forward neural network with the learning rate as a function. The stator resistance is also estimated using the two-layered neural network with learning rate as a function. The speed of the induction motor is also estimated by the neural network. Therefore, the accurate estimation of the rotor and stator resistance improved the quality of the sensorless induction motor drive. The results of simulation and experiment show that the estimated speed tracks the real speed of the induction motor; simultaneously, the error between the estimated rotor and stator resistance using neural network and the normal rotor and stator resistance is very small.

Keywords: rotor resistance estimation; stator resistance estimation; sensorless control; artificial neural network (ANN); indirect field-oriented control (IFOC) (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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