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

被引：11

作者：

Pham Van, Tuan ^{[1
]}

Vo Tien, Dung ^{[1
]}

Leonowicz, Zbigniew ^{[2
]}

Jasinski, Michal ^{[2
]}

Sikorski, Tomasz ^{[2
]}

Chakrabarti, Prasun ^{[3
,4
]}

机构：

[1] Vinh Univ Technol Educ, Fac Elect Engn, 117 Nguyen Viet Xuan St, Vinh City 890000, Vietnam

[2] Wroclaw Univ Sci & Technol, Fac Elect Engn, PL-50370 Wroclaw, Poland

[3] Techno India NJR Inst Technol Udaipur, Dept Comp Sci & Engn, Udaipur 313003, Rajasthan, India

[4] Thu Dau Mot Univ, Engn Technol Sch, Data Analyt & Artificial Intelligence Lab, Thu Dau Mot City 820000, Vietnam

来源：

ENERGIES | 2020年 / 13卷 / 18期

关键词：

rotor resistance estimation; stator resistance estimation; sensorless control; artificial neural network (ANN); indirect field-oriented control (IFOC); FIELD-ORIENTED CONTROL; FUZZY-LOGIC;

D O I：

10.3390/en13184946

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

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.

引用

页数：16

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