Flux Linkage Model Optimization using Binary Coded Genetic Algorithm for Switched Reluctance Motor

As of late, many researchers have shown a tremendous surge of interest in the field of switched reluctance motor. A precise model of switched reluctance motor will even boost the work time of this research progression as well as attract more researchers into this area. The phases of switched reluctance motor are approximately identical to each other with appropriate shift between them; hence most modeling will only concentrate on one selected phase of the drive. The flux linkage-current relationship is very much represented by function of rotor position with taking account of the magnetic characteristic; this makes the modeling to be a more challenging task. In this paper we compare two existing models of flux linkage current derivation - the optimization of measured flux using measured values and the estimation of flux via the Binary Coded Genetic Algorithm (BCGA).