Parameter identification of a cage induction motor using particle swarm optimization

Abstract

The current paper presents an adaptive system identification/parameter estimation algorithm for a three-phase cage induction motor based on particle swarm optimization (PSO). The performance of the proposed algorithm is emphasized by comparing its results with those of the well-known stochastic optimization techniques of genetic algorithm (GA) and simulated annealing (SA) for the benchmark application with six unknown parameters to identify. The dynamic inertia-weighted PSO algorithm significantly outperformed the GA and SA techniques. The achievement of the presented methodology in confronting a rather complicated non-linear dynamic engineering application underlines the ability of the algorithm to be used for a range of real-world problems, and moreover justifies and motivates the development of more advanced techniques.

Keywords

particle swarm optimization induction machine parameter identification swarm intelligence evolutionary algorithms

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References

Ursema

R. K.

Vadstrup

‘Parameter identification of induction motors using stochastic optimization algorithms.’

Appl Soft Comput. 4 (1) (2004): 49–64

Ljung

System identification, theory for the user, edition 2 1999 (Prentice Hall, New Jersey).

Palade

Bocaniala

C. D.

Jain

Computational intelligence in fault diagnosis (Springer, London 2006)

Engelbrecht

P. A.

Fundamentals of computational swarm intelligence (John Wiley & Sons 2005)

del Valle

Venayagamoorthy

K. G.

Hernandez

C. J.

Harley

G. R.

‘Particle swarm optimization: Basic concepts, variants and applications in power systems.’

IEEE Trans. Evol. Comput. 12 (2) (2008): 171–195

Elbeltagi

Hegazy

Grierson

‘Comparison among five evolutionary-based optimization algorithms.’

Adv. Engng Informat. 19 (1) (2005): 43–53

Engelbrecht

A. P.

Computational intelligence - An introduction, edition 2, 2007, pp. 289–358 (John Wiley & Sons).

Kennedy

Eberhart

R. C.

‘Particle swarm optimization.’

IEEE Int. Conf. Neural Networks 4 (27) (1995): 1942–48

Xiaodong

, A tutorial prepared for SEAL'06, School of Computer Science and IT, RMIT University, Melbourne, Australia, 2006

10.

Chaojun

Zulian

‘Particle swarm optimization algorithm based on the idea of simulated annealing.’

Int. J. Comput. Sci. Network Security 6 (10) (2006): 152–157

11.

Jiao

Lian

‘A dynamic inertia weight particle swarm optimization algorithm.’

Chaos Solutions Fractals 37 (3) (2006): 698–705

12.

Peer

E. S.

Bergh

F. V. D.

Engelbrecht

A. P.

‘Using neighborhoods with the guaranteed convergence PSO.’ In Proceedings of the IEEE Swarm Intelligence Symposium (SIS ′03), Indianapolis, Indiana, USA, 24-26 April, 2003, pp. 235–242.

13.

Zhan

Z. H.

Zhang

Chung

S. H. H.

‘Adaptive particle swarm optimization.’

IEEE Trans. Syst. Man Cybern. B Cybern. 39 (6) (2009): 1362–1381

14.

Bergh

F. V. D.

Engelbrecht

A. P.

‘A study of particle swarm optimization particle trajectories.’

Inform. Sci. 176 (8) (2006): 937–971

15.

Katari

Malireddi

Kanth

Bendapudi

Panda

‘Adaptive nonlinear system identification using comprehensive learning PSO.’ In Proceedings of ISCCSP, Malta, 12-14 March 2008, pp. 434–439 (IEEE).

16.

Banks

Vincent

Anyakoha

‘A review of particle swarm optimization. Part II: Hybridization, combinatorial, multicriteria and constrained optimization, and indicative applications.’

Nat. Comput. 7 (1) (2008): 109–124

17.

Mohan

Advanced electric drives analysis, control and modeling using Simulink (MNPERE, Minneapolis, Minnesota). 2006)

18.

Nikranjbar

Ebrahimi

Wood

A. S.

‘Model-based fault diagnosis of induction motor eccentricity using particle swarm optimization.’

Proc. IMechE, Part C: J. Mechanical Engineering Science 223 (C3) (2009): 607-615 DOI: 10.1243/09544062JMES1113