Dynamic Model Identification of Induction Motors using Intelligent Search Techniques with taking Core Loss into Account

Abstract

Traditionally, dynamic parameters of induction motors can be roughly estimated through conventional tests (no load test, block rotor test and retardation test) and core loss is neglected in the dynamic behaviours analysis. Due to the complication of dynamic behaviours of induction motors, inaccuracy of transient characteristics may obtain when using these dynamic parameters. In order to improving accuracy of dynamic behaviour analysis, however, the inclusion of core loss in the machine model needs to be re-addressed and an intelligent approach to estimated dynamic parameters needs to be adopted. In this paper, three of intelligent search techniques, which are i) Tabu Search (TS), ii) Adaptive Tabu Search (ATS) and iii) Genetic Algorithm (GA), are employed to demonstrate the effectiveness of intelligent identification compared with the conventional model with and without core loss parameter(RC). The simulation results from dynamic parameters including RC obtained by the GA in comparison with the experimental results are convinced the effectiveness for this aim.