Data Derived Identification Methodology for Online Estimation of Parameters of Induction Machine

Abstract

The paper highlights the revived requirement of parameter estimation of the induction machine. The data derived identification technique is introduced to provide the approximate induction machine model parameters. The active and reactive power output of the induction machine at some known condition is compared with active and reactive power output of the induction machine simulated in real-time digital simulator environment at different values of parameters at given operating conditions. Thus a real-world optimization problem is formed and is addressed by the mean-variance optimization scheme. The results are platformed on the MATLAB-RTDS environment which shares information via the TCP/IP connection. The estimated parameters will help in providing increased reliability in designing the advanced control scheme. The proposed methodology is tested in single as well as double cage rotor winding type of the induction motor and also in reduced voltage level scenario for the validation of the technique.