Optimal Design of a Brushless DC Motor, by Cuckoo Optimization Algorithm (RESEARCH NOTE)

Authors

1 Department of Electrical Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran

2 Department of Electrical Engineering, Sari Branch, Islamic Azad University, Sari, Iran

Abstract

This contribution deals with an optimal design of a brushless DC motor, using optimization algorithms, based on collective intelligence. For this purpose, the case study motor is perfectly explained and its significant specifications are obtained as functions of the motor geometric parameters. In fact, the geometric parameters of the motor are considered as optimization variables. Then, the objective function has been defined. This function consists of three terms i.e. losses, construction cost and the volume of the motor which should be minimized simultaneously. Three algorithms i.e. cuckoo, genetic and particle swarm have been studied in this paper. It is noteworthy that, cuckoo optimization algorithm has been used for the first time for brushless DC motor design optimization. A comparative study between the mentioned optimization approaches shows that, cuckoo optimization algorithm has been converged to optimal response in less than 250 iterations and its standard deviation is , while the convergence rate of the genetic and particle swarm algorithms are about 400 and 450 with standard deviations of  and , respectively for the case study motor. The obtained results show the best performance for cuckoo optimization algorithm among all mentioned algorithms in brushless DC motor design optimization.

Keywords


1.     Pyrhonen, J., Jokinen, T. and Hrabovcova, V., "Design of rotating electrical machines, John Wiley & Sons,  (2009).

2.     Tian, W., "Design of permanent magnet brushless DC motor control system based on dspic30f4012", Procedia Engineering,  Vol. 29,  (2012), 4223-4227.

3.     Afjei, E., Hashemipour, O., Saati, M. and Nezamabadi, M., "A new hybrid brushless DC motor/generator without permanent magnet", International Journal of Engineering Transactions B Applications,  Vol. 20, No. 1, (2007), 77-86.

4.     Zolfaghari, M. and Taher, S., "Fuzzy approximation model-based robust controller design for speed control of bldc motor", International Journal of Engineering-Transactions C: Aspects,  Vol. 28, No. 3, (2014), 426-432.

5.     Duan, H. and Gan, L., "Orthogonal multiobjective chemical reaction optimization approach for the brushless DC motor design", IEEE Transactions on Magnetics,  Vol. 51, No. 1, (2015), 1-7.

6.     Lee, T.-Y., Trung, P. X., Kim, J.-W., Kim, Y.-J. and Jung, S.-Y., "Search region management method for local search algorithm employing design optimization of brushless dc motor", IEEE Transactions on Magnetics,  Vol. 52, No. 3, (2016), 1-6.

7.     Ayala, H. V., Segundo, E. H., Mariani, V. C. and Coelho, L. d. S., "Multiobjective krill herd algorithm for electromagnetic optimization", IEEE Transactions on Magnetics,  Vol. 52, No. 3, (2016), 1-4.

8.     Ishikawa, T., Yonetake, K. and Kurita, N., "An optimal material distribution design of brushless DC motor by genetic algorithm considering a cluster of material", IEEE Transactions on Magnetics,  Vol. 47, No. 5, (2011), 1310-1313.

9.     Son, B., Park, G.-J., Kim, J.-W., Kim, Y.-J. and Jung, S.-Y., "Interstellar search method with mesh adaptive direct search for optimal design of brushless DC motor", IEEE Transactions on Magnetics,  Vol. 52, No. 3, (2016), 1-4.

10.   Yoon, K.-Y. and Kwon, B.-I., "Optimal design of a new interior permanent magnet motor using a flared-shape arrangement of ferrite magnets", IEEE Transactions on Magnetics,  Vol. 52, No. 7, (2016), 1-4.

11.   Kim, H.-s., You, Y.-M. and Kwon, B.-I., "Rotor shape optimization of interior permanent magnet BLDC motor according to magnetization direction", IEEE Transactions on Magnetics,  Vol. 49, No. 5, (2013), 2193-2196.

12.   Liu, X., Hu, H., Zhao, J., Belahcen, A., Tang, L. and Yang, L., "Analytical solution of the magnetic field and emf calculation in ironless BLDC motor", IEEE Transactions on Magnetics,  Vol. 52, No. 2, (2016), 1-10.

13.   Lee, T.-Y., Seo, M.-K., Kim, Y.-J. and Jung, S.-Y., "Motor design and characteristics comparison of outer-rotor-type BLDC motor and blac motor based on numerical analysis", IEEE Transactions on Applied Superconductivity,  Vol. 26, No. 4, (2016), 1-6.

14.   Rahideh, A., Korakianitis, T., Ruiz, P., Keeble, T. and Rothman, M., "Optimal brushless DC motor design using genetic algorithms", Journal of Magnetism and Magnetic Materials,  Vol. 322, No. 22, (2010), 3680-3687.

15.   Lai, S. H., "Design optimisation of a slotless brushless permanent magnet DC motor with helically-wound laminations for underwater rim-driven thrusters", University of Southampton,  (2006),

16.   Amiri, E. and Mahmoudi, S., "Efficient protocol for data clustering by fuzzy cuckoo optimization algorithm", Applied Soft Computing,  Vol. 41, (2016), 15-21.

17.   Arashloo, R. S., Martinez, J. L. R., Salehifar, M. and Moreno-Eguilaz, M., "Genetic algorithm-based output power optimisation of fault tolerant five-phase brushless direct current drives applicable for electrical and hybrid electrical vehicles", IET Electric Power Applications,  Vol. 8, No. 7, (2014), 267-277.

18.   Yousefi, A., Rahmani, A., Farzanegan, A. and Rostami, S., "Simulation and genetic algorithms for optimizing comminution circuit at gol-e-gohar iron plant (research note)", International Journal of Engineering-Transactions C: Aspects,  Vol. 26, No. 6, (2013), 663-670.

19.   Kazerooni, K., Rahideh, A. and Aghaei, J., "Experimental optimal design of slotless brushless pm machines based on 2-D analytical model", IEEE Transactions on Magnetics,  Vol. 52, No. 5, (2016), 1-16.

20.   Nahvi, H. and Mohagheghian, I., "A particle swarm optimization algorithm for mixed variable nonlinear problems", International Journal of Engineering,  Vol. 24, No. 1, (2011), 65-78.

21.   Akbarpour, H., Karimi, G. and Sadeghzadeh, A., "Discrete multi objective particle swarm optimization algorithm for FPGA placement", International Journal of Engineering Transactions C: Aspects,  Vol. 28, No. 3, (2015), 410-418.