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
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;
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
DOI :
10.1109/EVER.2013.6521561