Resistance and Speed Estimation of an Induction Motor: A Perspective on Classical and Dynamic Regression

A novel strategy to the speed estimation including classical polynomial regression and parameter identification using Dynamic Regression Extension and Mixing (DREM) is presented in the paper. The reactive power
$(Q=V_{sq}.i_{sd}-V_{sd}.i_{sq})$
is formed using instantaneous voltage and current values to get a regressor quantity and speed is taken as a response variable which enables an estimator fit. Since no additional gain calculation is required, the proposed regression-based speed estimation approach is simple to implement. The DREM is applied to estimate the Induction Motor (IM) parameters since it enhances transient response and global convergence of signals which are not square integrable. Linear Regression method requires to satisfy the persistency of excitation (PE) condition for parameter convergence and also struggles with signals that are not square integrable. Simulation results demonstrate the effectiveness of proposed speed estimator and DREM algorithm for parameter identification.