Optimal Design of Switched Reluctance Motor Using PSO Based FEM-EMC Modeling. Mouellef, S.; Bentounsi, A.; and Benalla, H. International Journal of Electrical and Computer Engineering (IJECE), 5(5):887--895, October, 2015. Equipe 1
Optimal Design of Switched Reluctance Motor Using PSO Based FEM-EMC Modeling [link]Paper  abstract   bibtex   
This paper aims to optimize the design of a prototype of a 6/4 Switched Reluctance Motor (SRM) using the Particle Swarm Optimization (PSO) algorithm. The geometrical parameters to optimize are the widths of the stator and rotor teeth due to their significant effects on the prototype design and the performances in terms of increased average torque and reduced torque ripple. The studied 3kW SRM is modeled using a numerical-analytical approach based on a coupled Finite Element Method with Equivalent Magnetic Circuit (FEM-EMC). The simulations are performed under MATLAB environment with user-friendly software. The optimal results found are discussed, compared against those obtained by the Genetic Algorithms (GA) and showed a significant improvement in average torque.
@article{mouellef_optimal_2015,
	title = {Optimal {Design} of {Switched} {Reluctance} {Motor} {Using} {PSO} {Based} {FEM}-{EMC} {Modeling}},
	volume = {5},
	copyright = {Copyright (c)},
	issn = {2088-8708},
	url = {http://www.iaesjournal.com/online/index.php/IJECE/article/view/7727},
	abstract = {This paper aims to optimize the design of a prototype of a 6/4 Switched Reluctance Motor (SRM) using the Particle Swarm Optimization (PSO) algorithm. The geometrical parameters to optimize are the widths of the stator and rotor teeth due to their significant effects on the prototype design and the performances in terms of increased average torque and reduced torque ripple. The studied 3kW SRM is modeled using a numerical-analytical approach based on a coupled Finite Element Method with Equivalent Magnetic Circuit (FEM-EMC). The simulations are performed under MATLAB environment with user-friendly software. The optimal results found are discussed, compared against those obtained by the Genetic Algorithms (GA) and showed a significant improvement in average torque.},
	language = {en},
	number = {5},
	urldate = {2016-06-17},
	journal = {International Journal of Electrical and Computer Engineering (IJECE)},
	author = {Mouellef, S. and Bentounsi, A. and Benalla, H.},
	month = oct,
	year = {2015},
	note = {Equipe 1},
	pages = {887--895},
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}
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