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Selected Nature-Inspired Algorithms in Function of PM Synchronous Motor Cogging Torque Minimisation

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Języki publikacji
EN
Abstrakty
EN
Both permanent magnet brushless DC motors and permanent magnet synchronous motors have attracted wide attention and are increasingly used in industrial high-performance applications in recent years. Those motors are known for their good electrical, magnetic and performance characteristics, but there is one parameter known as cogging torque that has a negative influence on the performance characteristics of the motor. This pulsating torque is generated as a result of the interaction between the stator teeth and the permanent magnets. The minimisation of the ripple of this torque in those permanent magnet motors is of great importance and is generally achieved by a special motor design which in the design process involves a variety of many geometrical motor parameters. In this research work, a novel approach will be introduced where two different nature-inspired algorithms, such as genetic algorithm (GA) and cuckoo search (CS) algorithm are used as an optimisation tool, in which the defined equation for the maximum value of the cogging torque is applied as an objective function. Therefore, a proper mathematical presentation of the maximum value of the cogging torque for the analysed synchronous motor is developed and implemented in the research work. For a detailed analysis of the three different motor models, the initial motor and the two optimised motor models are modelled and analysed using a finite element method approach. The cogging torque is analytically and numerically calculated and the results for all the models are presented.
Wydawca
Rocznik
Strony
204--217
Opis fizyczny
Bibliogr. 39 poz., rys., tab.
Twórcy
  • Ss.Cyril and Methodius University, Faculty of Electrical Engineering and Information Technologies, Skopje, North Macedonia
  • Ss.Cyril and Methodius University, Faculty of Electrical Engineering and Information Technologies, Skopje, North Macedonia
Bibliografia
  • Ackermann, B., Janssen, J. H., Sottek, R. and Van Steen, R. I. (1992). New Technique for Reducing Cogging Torque in a Class of Brushless DC Motors. IEE Proceedings of Electric Power Applications, 139(4), pp. 315–320.
  • Benlamine, R., Dubas, F., Randi, S. A., Lhotellier, D. and Espanet, C., (2013). Design by Optimization of an Axial-Flux Permanent-Magnet Synchronous Motor Using Genetic Algorithms. 2013 International Conference on Electrical Machines and Systems (ICEMS), 23–26, pp. 13–17.
  • Bianchi, N. and Bolognani, S. (2002). Design Techniques for Reducing the Cogging Torque in Surface-Mounted PM Motors. IEEE Transactions on Industry Applications, 38(5), pp. 1259–1265.
  • Bianchi, N. and Bolognani, S. (2000). Reducing Torque Ripple in PM Synchronous Motors by Pole Shifting. Proceedings of the International Conference on Electrical Machines ICEM, 28–30, pp. 1222–1226.
  • Chitara, D., Niazi, K. R., Swarnkar, A., and Gupta, N., (2018). Cuckoo Search Optimization Algorithm for Designing of a Multimachine Power System Stabilizer. IEEE Transactions on Industry Applications, 54(4), pp. 3056–3065.
  • Cvetkovski, G. and Petkovska, L. (2016). Multi-Objective Approach of Design Optimization of Axial Flux Permanent Magnet Motor. International Journal of Applied Electromagnetics and Mechanics, 51(s1), pp. S115–S123.
  • Cvetkovski, G. and Petkovska, L. (2021). Cogging Torque Minimization of PM Synchronous Motor Using Nature Based Algorithms. IEEE International Power Electronics and Motion Control Conference IEEE PEMC2020, 25–29, pp. 419–425.
  • Deb, K. and Jain, H. (2014). An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point Based Non-dominated Sorting Approach, Part I: Solving Problems With Box Constraints, IEEE Transactions on Evolutionary Computation, 18(4), pp. 577–601.
  • Deodhar, R. P. Stanton, D. A. Jahns, T. M. and Miller, T. J. (1996). Prediction of Cogging Torque Using the Flux-MMF Diagram Technique. IEEE Transactions on Industry Application, 32(3), pp. 569–575.
  • Di Barba, P., Mognaschi, M. E., Rezaei, N., Lowther, D. A. and Rahman, T. (2019). Many-Objective Shape Optimisation of IPM Motors for Electric Vehicle Traction. International Journal of Applied Electromagnetics and Mechanics, 60(S1), pp. S149–S162.
  • Eom, J. B., Hwang, S. M., Kim, T. J., Jeong, W. B. and Kang, B. S. (2001). Minimization of Cogging Torque in Permanent Magnet Motors by Teeth Pairing and Magnet Arc Design Using Genetic Algorithm. Journal of Magnetism and Magnetic Materials, 226–230(2), pp. 1229–1231.
  • Fei, W. and Luk, P. C. (2010). A New Technique of Cogging Torque Suppression in Direct-Drive Permanent-Magnet Brushless Machines. IEEE Transactions on Industry Applications, 46(4), pp. 1332–1340.
  • Gieras, J. F. (2004). Analytical Approach to Cogging Torque Calculation of PM Brushless Motors. IEEE Transactions on Industry Applications, 40(5), pp. 1310–1316.
  • Goto, M. and Kobayashi, K. (1983). An Analysis of the Cogging Torque of a DC Motor and a New Technique of Reducing the Cogging Torque. Electrical Engineering Japan, 103(5), pp. 113–120.
  • Ho, S. L., Chen, N. and Fu, W. N. (2010). An Optimal Design Method for the Minimization of Cogging Torques of a Permanent Magnet Motor Using FEM and Genetic Algorithm. IEEE Transactions on Applied Superconductivity, 20(3), pp. 861–864.
  • Holland, J. H. (1995). Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press.
  • Infolytica, User’s manual, Infolytica Corporation,Wappenham, 2016.
  • Ishikawa, T. and Slemon, G. R. (1993). A Method of Reducing Ripple Torque in Permanent Magnet Motors Without Skewing. IEEE Transactions on Magnetics, 29(2), pp. 2028–2031.
  • Islam, M. S., Mir, S. and Sebastian, T. (2004). Issues in Reducing the Cogging Torque of Mass-Produced Permanent-Magnet Brushless DC Motor. IEEE Transactions on Industry Applications, 40(3), pp. 813–820.
  • Jensen, W. R., Pham, T. Q. and Foster, S. N. (2019). Comparison of multi-objective optimization methods applied to electrical machine design. In: K. Deb, eds., Evolutionary Multi-Criterion Optimization. Cham: Springer, pp. 719–730.
  • Kamal, C., Thyagarajan, T., Selvakumari, M. and Kalpana, D., (2017). Cogging Torque Minimization in Brushless DC Motor Using PSO and GA Based Optimization. 2017 Trends in Industrial Measurement and Automation (TIMA), pp. 1–5.
  • Kang, G. H. and Hur, J. (2005). Analytical Prediction and Reduction of the Cogging Torque in Interior Permanent Magnet Motor. IEEE International Conference on Electric Machines and Drives, pp. 1620–1624.
  • Kumar, A. and Chakarverty, S. (2011). Design Optimization for Reliable Embedded System Using Cuckoo Search. 2011 3rd International Conference on Electronics Computer Technology, 1, pp. 264–268.
  • Lei, G., Bramerdorfer, G., Ma, B., Guo, Y. and Zhu, J. (2021). Robust Design Optimization of Electrical Machines: Multi-Objective Approach. IEEE Transactions on Energy Conversion, 36(1), pp. 390–401.
  • Li, T. and Slemon, G. (1988). Reduction of Cogging Torque in Permanent Magnet Motors. IEEE Transactions on Magnetics, 24(6), pp. 2901–2903.
  • Lukaniszyn, M., Jagiela, M. and Wrobel, R. (2004). Optimization of Permanent Magnet Shape for Minimum Cogging Torque Using a Genetic Algorithm. IEEE Transactions on Magnetics, 40(2), pp. 1228–1231.
  • Ma, G., Qiu, X., Yang, J., Bu, F., Dou, Y. and Cao, W. (2018). Structural Parameter Optimization to Reduce Cogging Torque of the Consequent Pole In-Wheel Motor. 2018 IEEE 18th International Power Electronics and Motion Control Conference (PEMC), pp. 170–175.
  • Mirahki, H., Moallem, M. and Rahimi, S. A. (2014). Design Optimization of IPMSM for 42 V Integrated Starter Alternator Using Lumped Parameter Model and Genetic Algorithms. IEEE Transactions on Magnetics, 50(3), pp. 114–119.
  • Mirjalili, S., Dong, J. S. and Lewis, A. (2020). Nature-Inspired Optimizers–Theories, Literature Reviews and Applications. Springer, Cham, Switzerland.
  • Motor Solve, User’s manual, Infolytica Corporation,Wappenham 2016.
  • Quintal-Palomo, R., Dybkowski, M. and Qwoździewicz, M. A. (2016). Parametric Analysis for the Design of a 4 Pole Radial Permanent Magnet Generator for Small Wind Turbines. Power Electronics and Drives, 36(2), pp. 175–186.
  • Siregar, M., Mohamed, T. Z., Wohon, D. R. and Nur, T. (2019). Optimizing the Cogging Torque Reduction of Integral Slot Number in Permanent Magnet Machine. 2019 International Conference on Technologies and Policies in Electric Power & Energy, pp. 1–5.
  • Sun, S., Jiang, F., Li, T. and Yang, K. (2019). Optimization of Cogging Torque in a Hybrid Axial and Radial Flux Permanent Magnet Machine. 22nd International Conference on Electrical Machines and Systems (ICEMS), pp. 1–5.
  • Uler, G. F., Mohammed, O. A. and Koh, C. S. (1995). Design Optimization of Electrical Machines Using Genetic Algorithms. IEEE Transactions on Magnetics, 31(3), pp. 2008–2011.
  • Wu, L. J., Zhu, Z. Q., Staton, D. A., Popescu, M. and Hawkins, D. (2012). Comparison of Analytical Models of Cogging Torque in Surface-Mounted PM Machines. IEEE Transactions on Industrial Electronics, 59(6), pp. 2414–2425.
  • Yang, X. S. (2014). Nature-Inspired Optimization Algorithms. Elsevier, London.
  • Yang, X. S. and Deb, S. (2010). Engineering optimization by Cuckoo Search. International Journal of Mathematical Modelling and Numerical Optimization, 1(4), pp. 330–343.
  • Zarko, D., Ban, D., and Lipo, T. A. (2008). Analytical Solution for Cogging Torque in Surface Permanent-Magnet Motors Using Conformal Mapping. IEEE Transactions on Magnetics, 44(1), pp. 52–65.
  • Zhu, Z. Q. and Howe, D. (2000). Influence of Design Parameters on Cogging Torque in Permanent Magnet Machines. IEEE Transactions on Energy Conversion, 15(4), pp. 407–412.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0f8c1ce8-9c25-4b21-a7b6-c121f735763b
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