Effective identification of induction motor parameters based on fewer measurements

Author

Huang, K.S. ; Wu, Q.H. ; Turner, D.R.

Author_Institution

Dept. of Electron. & Inf. Eng., Guangdong Univ. of Technol., Guangzhou, China

Volume

17

Issue

1

fYear

2002

fDate

3/1/2002 12:00:00 AM

Firstpage

55

Lastpage

60

Abstract

This paper applies genetic algorithms (GAs) to the problem of parameter identification for field orientation control (FOC) induction motors. Kron´s two-axis dynamic model in per-unit system is given, and the model´s parameters are estimated by a GA using the motor´s dynamic response to a direct on line start. Results with different levels of measurement noise are presented for the model both in the per-unit system and in actual values. For comparison, the results of a simple random search (SRS) method under the same condition are also given. The results show that the parameter identification accuracy, the convergence speed and the practicality of the algorithm have been improved significantly by use of the model in the per-unit system. The results also show that fewer measurements are required to identify the induction motor parameters accurately

Keywords

dynamic response; genetic algorithms; induction motors; machine vector control; parameter estimation; starting; Kron´s two-axis dynamic model; convergence speed; direct on line start; field orientation control; genetic algorithms; induction motors; measurement noise; motor dynamic response; parameter estimation; parameter identification; per-unit system; simple random search method; Circuit testing; Degradation; Genetic algorithms; Inductance; Induction motors; Noise measurement; Parameter estimation; Position control; Q measurement; Rotors;

fLanguage

English

Journal_Title

Energy Conversion, IEEE Transactions on

Publisher

ieee

ISSN

0885-8969

Type

jour

DOI

10.1109/60.986437

Filename

986437