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

Mohamed El-Nemr, Mohamed Afifi, Hegazy Rezk and Mohamed Ibrahim
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Mohamed El-Nemr: Electromagnetic Energy Conversion Laboratory, Tanta University, Tanta 31527, Egypt
Mohamed Afifi: Electromagnetic Energy Conversion Laboratory, Tanta University, Tanta 31527, Egypt
Hegazy Rezk: College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Wadi Aldawaser 11991, Saudi Arabia
Mohamed Ibrahim: Department of Electromechanical, Systems and Metal Engineering, Ghent University, 9000 Ghent, Belgium

Mathematics, 2021, vol. 9, issue 5, 1-20

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.

Keywords: optimal design; switched reluctance machine; NSGA-II optimization; finite element analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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