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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.