Comparative Analysis of Search Algorithm based Loss Minimization Techniques used in Vector Controlled Induction Motors

Abstract

This paper presents a comprehensive study for online loss minimization of induction motor (IM) drives. Each loss minimization algorithm has its advantages and disadvantages. In order to achieve effective conclusion for search algorithm based loss minimization techniques (SABLMTs), a comparison between five optimization algorithms including the genetic algorithm (GA), particle swarm optimization (PSO), chaotic optimization algorithm (COA), simulated annealing (SA), and imperialist competitive algorithm (ICA) is presented. For this purpose, the induction motor and its loss model considering core loss in the d-q reference frame are used. The optimum magnetization current along with the linkage flux are determined in a way that the induction motor loss is minimized considering different loads. The performance of the online optimization-based vector-controlled IM is analyzed using MATLAB/Simulink software in which the online algorithms are implemented by Embedded Matlab Function block in the Simulink environment. The simulation results show that using the SABLMTs provides better efficiency for IM drives especially in light loads without imposing any undesired effects on the dynamic performance of the IM drives. At the end, to make a proper conclusion, different SABLMTs are compared in terms of the processing time and accuracy.