Finite Element Based Overall Optimization of Switched Reluctance Motor Using Multi-Objective Genetic Algorithm (NSGA-II)

Abstract

The design of switched reluctance motor (SRM) is considered a complex problem to be solved using conventional design techniques. This is due to the large number of design parameters that should be considered during the design process. Therefore, optimization techniques are necessary to obtain an optimal design of SRM. This paper presents an optimal design methodology for SRM using the non-dominated sorting genetic algorithm (NSGA-II) optimization technique. Several dimensions of SRM are considered in the proposed design procedure including stator diameter, bore diameter, axial length, pole arcs and pole lengths, back iron length, shaft diameter as well as the air gap length. The multi-objective design scheme includes three objective functions to be achieved, that is, maximum average torque, maximum efficiency and minimum iron weight of the machine. Meanwhile, finite element analysis (FEA) is used during the optimization process to calculate the values of the objective functions. In this paper, two designs for SRMs with 8/6 and 6/4 configurations are presented. Simulation results show that the obtained SRM design parameters allow better average torque and efficiency with lower iron weight. Eventually, the integration of NSGA-II and FEA provides an effective approach to obtain the optimal design of SRM.

Multi-Objective Optimization of Switched Reluctance Machine Design Using Jaya Algorithm (MO-Jaya)

“…Winding design is achieved, as in [31,33]. The number of turns per phase is calculated based on SRM dimensions, such that the flux density value at the knee point of iron's B-H curve is maintained.…”

“…Subsequently, the number of horizontal layers, vertical layers, and coil dimensions can be calculated. Finally, clearance between adjacent coils is calculated at the point where the two coils are the closest to each other [31].…”

“…Referring to [31], the hysteresis losses can be calculated for various sectors of SRM. The flux density wave-forms depend on the phase current waveform and the speed of the motor.…”

“…The three objective functions calculation methods are presented in details. The results of optimization and the performance of the Jaya algorithm are compared with those of the non-dominated sorting genetic algorithm (NSGA-II) under the same constraints and for the same objectives [31]. The proposed method represents a general frame work for SRM multi-objective optimization.…”

Investigation of Characteristics of a Novel Torque Motor Based on an Annulus Air Gap

“…The genetic algorithm-based optimization method is proposed to maximize the torque density of the dual redundant permanent magnet synchronous motor (PMSM) [37]. El-Nemr et al proposed an optimal design methodology for switched reluctance motor (SRM) using the non-dominated sorting genetic algorithm (NSGA-II) optimization technique [38]. Yang et al proposed an active disturbance rejection control (ADRC) strategy based on the improved particle swarm optimization-genetic algorithm (IPSO-GA) for a bearing less induction motor (BIM) [39].…”