Genetic Algorithm Based Optimal Design of Switching Circuit Parameters for a Switched Reluctance Motor Drive

2006 ◽  
Author(s):  
Behzad Mirzaeian-Dehkordi ◽  
Peyman Moallem
Keyword(s):  
Genetic Algorithm ◽  
Optimal Design ◽  
Switched Reluctance Motor ◽  
Motor Drive ◽  
Switching Circuit ◽  
Switched Reluctance ◽  
Reluctance Motor

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

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 576
Author(s):  
Mohamed El-Nemr ◽  
Mohamed Afifi ◽  
Hegazy Rezk ◽  
Mohamed Ibrahim
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Optimal Design ◽  
Switched Reluctance Motor ◽  
Design Parameters ◽  
Objective Functions ◽  
Nsga Ii ◽  
Switched Reluctance ◽  
Multi Objective ◽  
Reluctance Motor

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.

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