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Unbalanced transients-based maximum likelihood identification of induction machine parameters

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3 Author(s)
R. Wamkeue ; Univ. du Quebec en Abitibi-Temiscamingue, Rouyn-Noranda, Que., Canada ; I. Kamwa ; M. Chacha

The paper describes an effective formulation of a maximum-likelihood identification algorithm for linear estimation of the equivalent-circuit parameters of cage-type (single cage and double cage) or deep-bar induction motors with measurement and process noises. A complete generalized model for symmetrical and asymmetrical test analysis of induction machines is developed for this purpose. The paper outlines the theory and reasoning behind the proposed statistical-based treatment of online data derived from generalized least-squares estimator and a Kalman filter. The method is successfully applied to online double-line independent finite-element (FE) short-circuit-simulated records of a deep-bar-type induction motor.

Published in:

IEEE Transactions on Energy Conversion  (Volume:18 ,  Issue: 1 )