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Appl. Sci. 2018, 8(7), 1073; https://doi.org/10.3390/app8071073

Estimating Parameters of the Induction Machine by the Polynomial Regression

1
Department of Electrical Engineering, I-Shou University, Kaohsiung 84001, Taiwan
2
Department of Electrical Engineering, Cheng Shou University, Kaohsiung 83347, Taiwan
*
Author to whom correspondence should be addressed.
Received: 1 June 2018 / Revised: 29 June 2018 / Accepted: 29 June 2018 / Published: 1 July 2018
(This article belongs to the Section Computer Science and Electrical Engineering)
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Abstract

Parameter identification of an induction machine is of great importance in numerous industrial applications. This paper used time-varied signals of voltage, current, and rotor speed to compute the equivalent circuit parameters, moment of inertia, and friction coefficient of an induction machine. The theoretical impedance-slip rate characteristic curve of the induction machine can be expressed as a polynomial fraction, so that a proper polynomial fraction can obtain complete and accurate parameters. A time-varied impedance can be found by the time-varied voltage and current. From the variation of impedance to the rotor speed, the parameters of the equivalent circuit can be found. According to the equivalent circuit and rotor speed, the torque can be determined via dynamic simulation. On the basis of torque and rotor speed with time, the moment of inertia and friction coefficient of the motor can then be obtained. Advantages of this method include the ability to obtain the optimal value via only one calculation, without the requirement of any initial value, and the avoidance of any local optimal solution. In this paper, the analysis of a practical induction machine was used as an example to illustrate the practical application. View Full-Text
Keywords: induction machine; parameter estimation; polynomial regression induction machine; parameter estimation; polynomial regression
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Wu, R.-C.; Tseng, Y.-W.; Chen, C.-Y. Estimating Parameters of the Induction Machine by the Polynomial Regression. Appl. Sci. 2018, 8, 1073.

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