Warianty tytułu
Języki publikacji
Abstrakty
Optimisation, or optimal design, has become a fundamental aspect of engineering across various domains, including power devices, power systems, and industrial systems. Engineers and academics have been actively involved in optimising these systems to achieve better performance, efficiency, and cost-effectiveness. Optimising electrical machines, including permanent magnet motors, is a complex task. It often involves solving intricate problems with various parameters and constraints. Engineers use different optimisation methods to tackle these challenges. Depending on the specific requirements and goals of a design project, engineers may employ either singleobjective or multi-objective optimisation approaches. Single-objective optimisation focuses on optimising a single objective, while multiobjective optimisation considers multiple conflicting objectives. In optimisation, objective functions are mathematical representations of what needs to be optimised. In this case, optimising the efficiency of the motor, reducing cogging torque, and minimising the total weight of active materials are defined as possible objective functions. Genetic algorithms are nature based algorithms that are commonly used in engineering to find optimal solutions to complex problems, including those with multiple objectives. In this paper, after conducting optimisations using different objective functions and methods, a comparative analysis of the results is performed. This helps in understanding the trade-offs and benefits of different design choices. Finite element analysis (FEA) is a computational method used to analyse the physical properties and behaviours of complex structures and systems. In this case, FEA is used to validate and analyse selected optimisation solutions to ensure they meet the desired characteristics and parameters. Overall, this work demonstrates the interdisciplinary nature of engineering, where mathematics, computer science (for optimisation algorithms), and physics (for FEA) converge to improve the performance and efficiency of electrical machines. It also underscores the importance of considering multiple objectives in design processes to find optimal solutions that strike a balance between competing goals.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
34--49
Opis fizyczny
Bibliogr. 26 poz., rys., tab.
Twórcy
autor
- Ss. Cyril and Methodius University, Faculty of Electrical Engineering and Information Technologies, Department of Electrical Machines, Transformers and Apparatuses, Skopje, North Macedonia, gogacvet@feit.ukim.edu.mk
autor
- Ss. Cyril and Methodius University, Faculty of Electrical Engineering and Information Technologies, Department of Electrical Machines, Transformers and Apparatuses, Skopje, North Macedonia
Bibliografia
- Bianchi, N. (2005). Electrical Machine Analysis Using Finite Elements, 1st ed. Boca Raton: CRC Press.
- Brisset, S. and Brochet, P. (1998). Optimization of Switched Reluctance Motors Using Deterministic Methods with Static and Dynamic Finite Element Simulations. IEEE Transactions on Magnetics, 34(5), pp. 2853–2856. doi: 10.1109/20.717664.
- Cvetkovski, G. (2001). Investigation of Methods and Contribution to the Development of Genetic Algorithms for Optimal Design of Permanent Magnet Synchronous Disc Motor. PhD. thesis. Skopje: Faculty of Electrical Engineering and Information Technologies, p. 253.
- Cvetkovski, G. and Petkovska, L. (2008). Efficiency maximisation in structural design optimisation of permanent magnet synchronous motor. In: Proceedings of the 18th International Conference on Electrical Machines-ICEM-2008 on CD, Vilamoura, Portugal, 6–9 September 2008, pp. 1–6.
- Cvetkovski, G. and Petkovska, L. (2010). Specific Power Optimal Design of Permanent Magnet Synchronous Motor Using GA. Przeglad ElektrotechnicznyOrgan Stowarzyszenia Elektrykow Polskich, 48 SIGMA-NOT-Wydawnictwo Czasopism Ksiazek Technicznych, 86(12), pp. 24–27.
- Cvetkovski, G. and Petkovska, L. (2013). Cogging torque minimisation of PM synchronous motor using genetic algorithm. In: Proceedings of the 16th International Symposium on Electromagnetic Fields in Mechatronics, Electrical and Electronic Engineering - ISEF’2013 on USB, paper ID 229, Ohrid, Macedonia, 12–14 September 2013, pp. 1–8.
- Cvetkovski, G. and Petkovska, L. (2021). Selected Nature-Inspired Algorithms in Function of PM Synchronous Motor Cogging Torque Minimisation. Power Electronics and Drives, 6(1), pp. 204–217. doi: 10.2478/pead-2021-0012.
- Di Barba, P., Mognaschi, M. E., Petkovska, L. and Cvetkovski, G. (2022). Cost-effective Optimal Synthesis of the Efficiency Map of Permanent Magnet Synchronous Motors. International Journal of Applied Electromagnetics and Mechanics, 69(2), pp. 189–199. doi: 10.3233/JAE-210201.
- 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. doi: 10.3233/JAE-191113.
- Gebregergis, A., Chowdhury, M. H., Islam, M. S. and Sebastian, T. (2014). Modeling of PermanentMagnet Synchronous Machine Including Torque Ripple Effects. IEEE Transactions on Industry Applications, 51, pp. 232–239. doi: 10.1109/ TIA.2014.2334733.
- Gieras, J. (2004). Analytical Approach to Cogging Torque Calculation of PM Brushless Motors. IEEE Transactions on Industry Applications, 40(5), pp. 1310–1316. doi: 10.1109/TIA.2004.834108.
- Gottvald, A., Preis, K., Magele, C., Biro, O. and Savini, A. (1992). Global Optimization Methods for Computational Electromagnetics. IEEE Transactions on Magnetics, 28(2), pp. 1537–1540. doi: 10.1109/20.123990.
- Hameyer, K. and Belmans, R. (1999). Numerical Modelling and Design of Electrical Machines and Devices. Billerica, MA, USA: WIT Press.
- Holland, J. H. (1973). Genetic Algorithms and the Optimal Allocation of Trials. SIAM Journal on Computation, 2(2), pp. 88–105. doi: 10.1137/0202009.
- Holland, J. H. (1995). Adaptation in Natural and Artificial Systems. Ann Arbour: University of Michigan Press.
- Im, D.-H., Park, S.-C. and Im, J.-W. (1993). Design of Single-Sided Linear Induction Motor Using the Finite Element Method and SUMT. IEEE Transactions on Magnetics, 29(2), pp. 1762–1766.
- Infolityca. (2016). User’s Manual. Inflolytica, Towcester, UK.
- Janikow, C. Z. and Michalewicz, Z. (1991). An experimental comparison of binary and floating point representations in genetic algorithms. In: Proceedings of the Fourth International Conference on Genetic Algorithms, San Diego, CA, USA, July 1991, pp. 31–35.
- Liu, X. and Slemon, G. (1991). An Improved Method of Optimization for Electrical Machines. IEEE Transactions on Energy Conversion, 6(3), pp. 492–496. doi: 10.1109/60.84326.
- MotorSolve. (2016). User’s Manual. Infolytica.
- Ruuskanen, V., Nerg, J., Rilla, M. and Pyrhonen, J. (2016). Iron Loss Analysis of the PermanentMagnet Synchronous Machine Based on FiniteElement Analysis Over the Electrical Vehicle Drive Cycle. IEEE Transactions on Industrial Electronics, 63, pp. 4129–4136. doi: 10.1109/TIE.2016.2549005.
- Shklyarskiy, Y. E., Shklyarskiy, A. Y. and Lutonin, A. S. (2021). Sizing Parameters of Interior Permanent Magnet Synchronous Motor Based on Torque-speed Characteristics. Journal Physics: Conference Series, 1753(012026), pp. 1–9.
- Sim, D. J., Jung, H. K., Hahn, S. Y. and Won, J. S. (1997). Application of Vector Optimization Employing Modified Genetic Algorithms to Permanent Magnet Motor Design. IEEE Transactions on Magnetics, 33(2), pp. 1888–1891. doi: 10.1109/20.582654.
- Üler, G. F., Mohammed, O. A. and Koh, C. S. (1995). Design Optimisation of Electrical Machines using Genetic Algorithms. IEEE Transactions on Magnetics, 31(3), pp. 2008–2011. doi: 10.1109/20.376437.
- Üler, G. F. and Mohammed, O. A. (1996). Ancillary Techniques for the Practical Implementation of GAs to the Optimal Design of Electromagnetic Devices. IEEE Transactions on Magnetics, 32(3), pp. 1194–1197. doi: 10.1109/20.497457.
- Wurtz, F., Richomme, M., Bigeon, J. and Sabonnadiere, J. C. (1997). A Few Results for Using Genetic Algorithms in the Design of Electrical Machines. IEEE Transactions on Magnetics, 33(2), pp. 1892–1895. doi: 10.1109/20.582656.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-16876642-bb20-49da-b4d7-31e9186c87e6