DocumentCode
602171
Title
Induction machine parameter identification: A comparison between GAs and PSO approaches
Author
Yousfi, Laatra ; Bouchemha, Amel ; Bechouat, Mohcene ; Boukrouche, A.
Author_Institution
Lab. Inverses Problems: Modeling, Inf. & Syst. (PI:MIS), Univ. of Tebessa, Tebessa, Algeria
fYear
2013
fDate
27-30 March 2013
Firstpage
1
Lastpage
5
Abstract
This paper, deals with meta-heuristics methods such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) for parameters identification of induction machine. The considered method consists on minimizing quadratic criterion that represents the difference between measured rotor mechanical speed and those computed from the simulated model. The obtained results by simulation show that the method based on particle swarm optimization is more efficient than genetic algorithms in terms of convergence speed and gives optimal solution.
Keywords
asynchronous machines; genetic algorithms; particle swarm optimisation; GA approach; PSO approach; genetic algorithms; induction machine parameter identification:; particle swarm optimization; quadratic criterion minimization; rotor mechanical speed; Genetic algorithms; Induction machines; Mathematical model; Parameter estimation; Particle swarm optimization; Rotors; Stators; Comparison; Experimental Validation; Genetic Algorithms; Identification; Induction Machine; Particle Swarm Optimization;
fLanguage
English
Publisher
ieee
Conference_Titel
Ecological Vehicles and Renewable Energies (EVER), 2013 8th International Conference and Exhibition on
Conference_Location
Monte Carlo
Print_ISBN
978-1-4673-5269-7
Type
conf
DOI
10.1109/EVER.2013.6521561
Filename
6521561
Link To Document