An approach based on non-dominated sorting genetic algorithm III for design of permanent magnet synchronous motor

Today due to industrial developments, the use of electric motors has increased in all fields. The increase also preceded the development of higher-specification motors. Although weight, cogging torque, torque ripples and drive technology etc. for the working area are important, the demand for the production of highly efficient and cost-effective motors has risen further due to the energy phenomenon in the world. High-quality algorithms are needed to achieve these objectives as well, because electric motor designs are multi parameter and nonlinear engineering problems. This study aims to provide a multi-purpose intelligent design with NSGAII and NSGAIII by selecting outputs such as efficiency and cost of permanent magnet synchronous motor as an objective function. The design was intended for low speed and high torque/volume applications and the motor geometry was thus chosen as surface-mounted and double-layer concentrated winding. The optimization results were tested with a finite element program. Both methods resulted in a 3% increase in efficiency and a 37% reduction in cost versus initial design. Also, according to the results obtained, although NSGA-II and NSGA-III achieved similar results, NSGA-III results showed a more robust and stable course than NSGA-II results. The compatibility of the design optimization and the results of numerical analysis are acceptable and highly satisfactory. So, it provides outputs to demonstrate the features of an electric motor design optimization.

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[1] D. C. Hanselman (2006). Brushless permanent magnet motor design. Magna Physics Publishing, Ohio.

[2] V. S. Sempere, M. B. Payán, J. R. C. Bueno (2017). Cogging torque cancellation by magnet shaping in surface-mounted permanent-magnet motors. IEEE Transactions on Magnetics, Vol. 53, Issue 7, doi:10.1109/TMAG.2017.2676090.

[3] Herlina, R. Setiabudy, A. Rahardjo (2017). Cogging torque reduction by modifying stator teeth and permanent magnet shape on a surface mounted PMSG. International Seminar on Intelligent Technology and Its Applications, pp: 227-232, doi:10.1109/ISITIA.2017.8124085.

[4] B. N. Cassimere, S. Sudhoff (2009). Population-based design of surface-mounted permanent-magnet synchronous machines. IEEE Transactions on Energy Conversion, Vol. 24, No. 2, pp 338-346, doi:10.1109/TEC.2009.2016150.

[5] L. Jing, R. Qu, W. Kong, D. Li, H. Huang (2017). Genetic-algorithm-based analytical method of SMPM motors. IEEE International Electric Machines and Drives Conference, doi:10.1109/IEMDC.2017.8002030.

[6] W. Zhao, J. W. Kwon, X. Wang, T. A. Lipo, B. I. Kwon (2017). Optimal design of a spoke-type permanent magnet motor with phase-group concentrated-coil windings to minimize torque pulsations. IEEE Transactions on Magnetics, Vol. 53, Issue 6, doi:10.1109/TMAG.2017.2664075.

[7] M. Mutluer, O. Bilgin (2016). An intelligent design optimization of a permanent magnet synchronous motor by artificial bee colony algorithm. Turkish Journal of Electrical Engineering & Computer Sciences, Vol. 24, pp 1826-1837, doi:10.3906/elk-1311-150.

[8] S. Owatchaiphong, N. H. Fuengwarodsakul (2009). Multiobjective based optimization for switched reluctance machines using fuzzy and genetic algorithms. International Conference on Power Electronics and Drive Systems, doi:10.1109/PEDS.2009.5385926.

[9] M. T. Chui, J. A. Chiang, J. M. Lee, Z. L. Gaing (2014). Multiobjective optimization design of interior permanent-magnet synchronous motors for improving the effectiveness of field weakening control. 17th International Conference on Electrical Machines and Systems, doi:10.1109/ICEMS.2014.7013534.

[10] Trisnal, Marimin, Y. Arkeman (2016). Solving fuzzy multi-objective optimization using non-dominated sorting genetic algorithm II. International Conference on Advanced Computer Science and Information Systems, doi: 10.1109/ICACSIS.2016.7872798.

[11] T. Sağ, M. Çunkaş (2009). A tool for multiobjective evolutionary algorithms. Advances in Engineering Software, Vol. 40, Issue 9, pp 902-912, doi:10.1016/j.advengsoft.2009.01.001.

[12] K. Deb, S. Agrawal, A. Pratap, T. Meyarivan (2000). A fast elitist nondominated sorting genetic algorithm for multiobjective optimization: NSGA-II. International Conf. on Parallel Problem Solving from Nature, pp 849-858.

[13] K. Deb and H. Jain (2014). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I. IEEE Transactions on Evolutionary Computation, Vol. 18, Issue 4, pp 577-601, doi:10.1109/TEVC.2013.2281535.

[14] H. Jain and K. Deb (2014) An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part II. IEEE Transactions on Evolutionary Computation, Vol. 18, Issue 4, pp 602-622, doi:10.1109/TEVC.2013.2281534.

[15] R. H. Bhesdadiya, I. N. Trivedi, P. Jangir, N. Jangir, A. Kumar(2016). An NSGA-III algorithm for solving multi-objective economic/environmental dispatch problem. Cogent Engineering, Vol. 3, Issue 1, doi:10.1080/23311916.2016.1269383.

[16] J. F. Gieras, M. Wing (2002). Permanent magnet motor technology design and applications, second edition, revised and expanded, Marcal Dekker Inc., New York.

[17] F. Libert (2004). Design, optimization and comparison of permanent magnet motors for a low-speed direct-driven mixer. Technical Licentiate, School of Computer Science, Electrical Engineering and Engineering Physics, KTH, Sweden.

[18] R. Chaudhary, R. Sanghavi, S. Mahagaokar (2017). Optimization of induction motor using genetic algorithm and GUI of optimal induction motor design in MATLAB. Advances in Systems, Control and Automation, pp 127-132.

[19] Z. S. Liu (2017). Design and performance simulation of direct drive hub motor based on improved genetic algorithm. The Fourth Euro-China Conference on Intelligent Data Analysis and Applications, pp 303–313, doi:10.1007/978-3-319-68527-4_33.

[20] M. A. Şahman, M. Çunkaş, Ş. İnal, F. İnal, B. Coşkun and U. Taşkiran (2009). Cost optimization of feed mixes by genetic algorithms. Advances in Engineering Software, 40(10), 965-974.

[21] M. Mutluer, M. A. Şahman and M. Çunkaş (2020). Heuristic optimization based on penalty approach for surface permanent magnet synchronous machines. Arabian Journal for Science and Engineering, 45(8), 6751-6767.

[22] R. L. Haupt, S. E. Haupt (1998). Practical Genetic Algorithms. A Willey-Interscience Publication, USA.

[23] S. F. Contreras, C. A. Cortes, M. A. Guzmán (2017). Modelling of squirrel cage induction motors for a bio-inspired multiobjective optimal design. IET Electric Power Applications, Vol. 11, Issue 4, pp 512-523.

[24] B. Anvari, H. A. Toliyat (2017). Simultaneous optimization of geometry and firing angles of in-wheel switched reluctance motor. IEEE Energy Conversion Congress and Exposition, pp 760-767, doi:10.1109/ECCE.2017.8095861.

[25] Y. Duan, R. G. Harley, T. G. Habetler (2009). Method for multiobjective optimized designs of surface mount permanent magnet motors with concentrated or distributed stator windings. IEEE International Electric Machines and Drives Conference, doi:10.1109/IEMDC.2009.5075225.

[26] K. Deb (2001). Multiobjective optimization using evolutionary algorithms. John Wiley & Sons, England.

[27] N. Srinivas, K. Deb (1994). Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, Vol. 2, Issue 3, pp 221-248, doi:10.1162/evco.1994.2.3.221.

[28] K. Deb (2011). Multiobjective optimization using evolutionary algorithms: an introduction. multiobjective evolutionary optimisation for product design and manufacturing. pp 3–34.

[29] R. Pichot, L. Schmerber, D. Paire, A. Miraoui (2018). Robust BLDC motor design optimization including raw material cost variations. XIII International Conference on Electrical Machines, doi:10.1109/ICELMACH.2018.8507039.