Economic Design of Three-Phase Induction Motor by Particle Swarm Optimization

Abstract

A Particle Swarm Optimization (PSO) based design of three-phase induction motors are proposed. The induction motor design is treated as a non-linear and multivariable constrained optimization problem. The annual material cost and the total annual cost of the motor are chosen as two different objective functions. The PSO is used to find a set of optimal design variables of the motor which are then used to predict performance indices and the objective functions. The proposed method is demonstrated for two sample motors, and it is compared with the genetic algorithm (GA) and the conventional design methods. The results show that the PSO-based method effectively solved the induction motor design problems and outperforms the other methods in both the solution quality and computation efficiency.

A Particle Swarm Optimization (PSO) based design of three-phase induction motors are proposed. The induction motor design is treated as a non-linear and multivariable constrained optimization problem. The annual material cost and the total annual cost of the motor are chosen as two different objective functions. The PSO is used to find a set of optimal design variables of the motor which are then used to predict performance indices and the objective functions. The proposed method is demonstrated for two sample motors, and it is compared with the genetic algorithm (GA) and the conventional design methods. The results show that the PSO-based method effectively solved the induction motor design problems and outperforms the other methods in both the solution quality and computation efficiency.

Cite this paper

nullV. Sakthivel, R. Bhuvaneswari and S. Subramanian, "Economic Design of Three-Phase Induction Motor by Particle Swarm Optimization,"*Journal of Electromagnetic Analysis and Applications*, Vol. 2 No. 5, 2010, pp. 301-310. doi: 10.4236/jemaa.2010.25039.

nullV. Sakthivel, R. Bhuvaneswari and S. Subramanian, "Economic Design of Three-Phase Induction Motor by Particle Swarm Optimization,"

References

[1] R. Ramarathnam and B. G. Desai, “Optimization of Poly- Phase Induction Motor Design-A Nonlinear Programming Approach,” IEEE Transactions Power Apparatus and Systems, Vol. 90, No. 2, 1971, pp. 570-578.

[2]
B. G. Bharadwal, K. Venkatesen and R. B. Saxena, “Experience with Direct and Indirect Search Methods Applied to Cage Induction Motor Design Optimization,” Electric Machines and Electromechanics, Vol. 4, No. 1, 1979, pp. 85-93.

[3]
C. Li and A. Rahman, “Three-Phase Induction Motor Design Optimization Using the Modified Hooke-Jeeves Method,” Electrical Machines and Power Systems, Vol. 18, No. 1, 1990, pp. 1-12.

[4]
J. Appelbaum, E. F. Fuchs and J. C. White, “Optimization of Three-Phase Induction Motor, Part I: Formation of the Optimization Technique,” IEEE Transactions on Energy Conversion, Vol. 2, No. 3, 1987, pp. 407-415.

[5]
J. Appelbaum, I. A. Khan, E. F. Fuchs and J. C. White, “Optimization of Three-Phase Induction Motor, Part II: The Efficiency and Cost of an Optimum Design,” IEEE Transactions on Energy Conversion, Vol. 2, No. 3, 1987, pp. 415-422.

[6]
N. H. Feith and H. M. EI-Shewy, “Induction Motor Optimum Design Including Active Power Loss Effect,” IEEE Transactions on Energy Conversion, Vol. 1, No. 3, 1986, pp. 155-160.

[7]
G. F. Uler, O. A. Mohammed and C.-S. Koh, “Design Optimization of Electrical Machines Using Genetic Algorithms,” IEEE Transactions on Magnetics, Vol. 31, No. 3, 1995, pp. 2008-2011.

[8]
J. P. Wieczorek, O. Gol and Z. Michalewiez, “An Evolutionary Algorithm for the Optimal Design of Induction Motors,” IEEE Transactions on Magnetics, Vol. 34, No. 6, 1998, pp. 3882-3887.

[9]
G. T. Bellarmine, R. Bhuvaneswari and S. Subramanian, “Radial Basis Function Network Based Design Optimization of Induction Motor,” Proceedings of IEEE SOUTHEASTCON 2006, Memphis, Tennessee, 2006, pp. 75-80.

[10]
R. Bhuvaneswari and S. Subramanian, “Fuzzy Logic Approach to Three-Phase Induction Motor Design,” Proceedings of the International Conference on Computer Applications in Electrical Engineering Recent Advances - CERA-05, IIT Roorkee, 28 September-1 October 2005, pp. 505-509.

[11]
J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” Proceedings of the IEEE Conference on Neural Networks-ICNN’95, Perth, Vol. 4, 1995, pp. 1942-1948.

[12]
Y. Shi and R. C. Eberhart, “Empirical Study of Particle Swarm Optimization,” Proceedings of the IEEE International Congress Evolutionary Computation, Anchorage, Vol. 3, 1999, pp. 101-106.

[13]
R. Eberhart and J. Kennedy, “A New Optimizer Using Particle Swarm Optimization,” Proceedings of the 1995 Sixth International Symposium on Micro Machine and Human Science, Nagoya, pp. 39-43.

[14]
R. C. Eberhart and Y. Shi, “Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization,” Proceedings of the IEEE International Congress Evolutionary Computation, San Diego, Vol. 1, 2000, pp. 84-88.

[15]
K. T. Chaturvedi, M. Pandit and L. Srivastava, “Particle Swarm Optimization with Time Varying Acceleration Coefficients for Non-Convex Economic Power Dispatch,” Electrical Power and Energy Systems, Vol. 31, No. 6, 2009, pp. 249-257.

[16]
M. G. Say, “Alternating Current Machines,” Pitman, London, 1983.