Unbalanced Transients-Based Maximum Likelihood Identification of Induction Machine Parameters

This 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 a generalized least-squares estimator and a Kalman filter. The method is successfully applied to online double-line independent finite-element short-circuit simulated records of a deep-bar type induction motor.