Experimental evaluation on parameter identification of induction motor using continuous-time approaches

This paper evaluates parameter identification of induction motor (IM) using two different methods. The proposed methods provide an accurate estimation on the parameters for the IM steady state model. Both algorithms were tested with real data and then used to estimate the parameters of the motor. The disturbances in the acquired signals were reduced using some signal processing techniques. The processed signals were then applied in both identification procedures. The first method is based on optimization by nonlinear least squares algorithm that permits establishing convergence constrains, avoiding impossible physical results. The second is an indirect method that allows keeping the physical meaning of the continuous time parameters when converting to discrete time domain. The effectiveness of the proposed methods is verified by experimental tests and results of the methods are discussed.