Intelligent Identification of Induction Machine Parameters for the Synthesis and Experimental Validation of State Observers

Abstract

This thesis deals with the modeling, parameter identification, and state observation of a three phase induction machine. Amathematical model was developed in the two-axis reference frame to describe the machine dynamics. The machine parameters were initially determined using no-load and locked-rotor tests. To improve model accuracy, a Particle Swarm Optimization (PSO) algorithm was applied to refine the stator, rotor, and mutual inductances. The obtained parameters were validated experimentally using a dSPACE platform, and a good agreement was observed between simulated and measured current, flux, and speed responses. In addition, deterministic full-order and high-gain observers were designed to estimate non measurable states, particularly the rotor flux. The results demonstrate the effectiveness of the proposed modeling, identification, and observation techniques for induction machine analysis and control.