Elephant Herding Optimization (EHO) Based Parameters Estimation of Induction Machine Considering the Nonlinear Core-Loss Model

Abstract

Estimation of parameters for an induction machine is essential in performance analysis and control scheme design in industrial applications. In this paper, elephant herding optimization (EHO) technique-based parameter estimation technique for an induction motor is studied, and the optimum parameters are obtained using a least mean square technique (LMST). The input impedance is studied at different slip samples and a steady-state model of the squirrel-cage induction machine is developed by incorporating the nonlinear core-loss resistance. The real machine parameters are obtained from the practical experimentation on a squirrel cage induction machine. Real machine parameters are fed to the optimization algorithm as the initial values. By considering the nonlinear core-loss parameter in the equivalent circuit model, the proposed method suggests a more accurate parameter estimation technique. The effectiveness of the proposed EHO-based induction machine parameters optimization techniques is validated by the experimental and simulation results.