PROJECT TITLE :

On the Identifiability of Steady-State Induction Machine Models Using External Measurements

ABSTRACT:

A standard practice in induction machine parameter identification techniques is to use external measurements of voltage, current, speed, and/or torque. Using this approach, it has been shown that it is attainable to obtain an infinite range of mathematical solutions representing the machine parameters. This paper examines the identifiability of 2 commonly used induction machine models, particularly the T-model (the traditional per phase equivalent circuit) and the inverse Γ-model. A unique approach primarily based on the alternating conditional expectation (ACE) algorithm is used here for the primary time to study the identifiability of the two induction machine models. The results obtained from the proposed ACE algorithm show that the parameters of the commonly utilized T-model are unidentifiable, not like the parameters of the inverse Γ-model which are uniquely identifiable from external measurements. The identifiability analysis results are experimentally verified using the measured operating characteristics of a 1.1-kW three-phase induction machine along side the Levenberg-Marquardt algorithm, that is developed and applied here for this purpose.