Parameter estimation for electric motor condition monitoring

Juggrapong Treetrong, Jyoti K. Sinha, Fengshu Gu, Andrew Ball

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

This paper presents parameter identification technique to quantify the faults in motor condition monitoring. Genetic Algorithm (GA) has been used as a key technique to estimate the motor parameters. The zero-sequence voltage equation for the stator has been used as a model to estimate motor stator parameters - the stator resistance and the stator leakage inductance. The comparison of the parameter estimation by the earlier Recursive Least Square (RLS) method and the proposed GA technique has been discussed. The GA technique shows better accuracy in the estimation. The estimation has been tested on both simulations and a real test motor.

Original languageEnglish
Pages (from-to)75-83
Number of pages9
JournalAdvances in Vibration Engineering
Volume11
Issue number1
Publication statusPublished - Jan 2012

Fingerprint

electric motors
stators
Electric motors
Condition monitoring
Parameter estimation
Stators
genetic algorithms
Genetic algorithms
parameter identification
least squares method
estimates
inductance
Inductance
Identification (control systems)
leakage
Electric potential
electric potential
simulation

Cite this

Treetrong, Juggrapong ; Sinha, Jyoti K. ; Gu, Fengshu ; Ball, Andrew. / Parameter estimation for electric motor condition monitoring. In: Advances in Vibration Engineering. 2012 ; Vol. 11, No. 1. pp. 75-83.
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Parameter estimation for electric motor condition monitoring. / Treetrong, Juggrapong; Sinha, Jyoti K.; Gu, Fengshu; Ball, Andrew.

In: Advances in Vibration Engineering, Vol. 11, No. 1, 01.2012, p. 75-83.

Research output: Contribution to journalArticle

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AU - Treetrong, Juggrapong

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AU - Gu, Fengshu

AU - Ball, Andrew

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KW - Genetic algorithm

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