This paper describes three methods for estimating the lumped model parameters of an induction motor using startup transient data. A three-phase balanced induction motor is assumed. Measurements of the stator currents and voltages are required for the identification procedure, but no measurements from the motor shaft are needed. The first method presented applies simple models with limited temporal domains of validity and obtains parameter estimates by extrapolating the model error bias to zero, This method does not minimize any specific error criterion and is presented as a means of finding a good initial guess for a conventional iterative maximum-likelihood or least-squares estimator. The second method presented minimizes equation errors in the induction motor model in the least-square sense using a Levenburg-Marquardt iteration, The third identification method is a continuation of the Levenburg-Marquardt method, motivated by observed properties of some pathological loss functions. The third method minimizes errors in the observations in the least-squared sense and is, therefore, a maximum-likelihood estimator under appropriate conditions of normality. The performance of the identification schemes is demonstrated with both simulated and measured data, and parameters obtained using the methods are compared with parameters obtained from standard tests.
