A novel hybrid cuckoo search algorithm for optimization of a line-start PM synchronous motor

• Autorzy: Knypiński Łukasz

Identyfikatory

DOI

10.24425/bpasts.2023.144586

• Języki publikacji: EN

Abstrakty

EN

The paper presents a novel hybrid cuckoo search (CS) algorithm for the optimization of the line-start permanent magnet synchronous motor (LSPMSM). The hybrid optimization algorithm developed is a merger of the heuristic algorithm with the deterministic Hooke–Jeeves method. The hybrid optimization procedure developed was tested on analytical benchmark functions and the results were compared with the classical cuckoo search algorithm, genetic algorithm, particle swarm algorithm and bat algorithm. The optimization script containing a hybrid algorithm was developed in Delphi Tiburón. The results presented show that the modified method is characterized by better accuracy. The optimization procedure developed is related to a mathematical model of the LSPMSM. The multi-objective compromise function was applied as an optimality criterion. Selected results were presented and discussed.

Słowa kluczowe

EN

hybrid cuckoo search algorithm; heuristic algorithms; multi-objective optimization; permanent magnet synchronous motor; PMSM

PL

algorytm kukułki hybrydowy; algorytm Cuckoo; algorytm heurystyczny; optymalizacja wielocelowa; silnik synchroniczny z magnesem trwałym; PMSM

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2023
• Tom: Vol. 71, nr 1
• Strony: art. no. e144586
• Opis fizyczny: Bibliogr. 39 poz., rys., tab.

Twórcy

autor

Knypiński Łukasz

• Poznan University of Technology, Institute of Electrical Engineering and Electronics, Piotrowo 3a, 60-965 Poznan, Poland

• https://orcid.org/0000-0001-5741-9548

Bibliografia

• [1] I. Stojanović, I. Brajević, P. Stanimirović, L. Kazakovtsev, and Z. Zdravev, “Application of heuristic and metaheuristic algorithms in solving constrained weber problem with feasible region bounded by arcs,” Math. Probl. Eng., vol. 2017, p. 8306732, 2017, doi: 10.1155/2017/8306732.
• [2] D. Fu et al., “Optimization design of a novel flux-switching transverse-flux permanent magnet tube linear motor,” IEEE Trans. Magn., vol. 57, no. 6, pp. 1-6, 2021, doi: 10.1109/TMAG.2021.3061812.
• [3] M. Akinsolu, B. Liu, P. Lazaridis, K. Mistry, M. Mognaschi, P. Di Barba, and Z. Zaharis, “Efficient Design Optimization of High-Performance MEMS Based on a Surrogate-Assisted Self-Adaptive Differential Evolution,” IEEE Access, vol. 8, pp. 80256 – 80268, 2020„ doi: 10.1109/ACCESS.2020.2990455.
• [4] M. Mutluer, A. Sahman, and M. Cunkas, “Heuristic optimization based on penalty approach for surface permanent magnet synchronous machines,” Arab. J. Sci. Eng., vol. 45, pp. 6751-6767, 2020, doi: 10.1007/s13369-020-04689-y.
• [5] Ł. Knypiński, “Constrained optimization of line-start PM motor based on the gray wolf optimizer,” Ekspolatacja i Niezawodność – Maintaince and Reliability, vol. 23, no. 1, pp. 1–10, 2021, doi: 10.17531/ein.2021.1.1.
• [6] J. Deng, X. Liu, and G. Zha, “Robust design optimization of electromagnetic actuators for renewable energy systems considering the manufacturing cost,” Energies, vol. 12, p. 4353; 2019, doi: 10.3390/en12224353.
• [7] B. Mohamodhosen, A. Tounzi, and F. Gillon, “Rotor head shape optimization in a salient pole synchronous machine,” Int. J. Appl. Electromagn. Mech., vol. 64, vol. S1, pp. S3-S13, 2020, doi: 10.3233/JAE-209501.
• [8] R. Devarapalli et al., “Allocation of real power generation based on computing over all generation cost: An approach of Salp Swarm Algorithm,” Arch. Electr. Eng., vol. 70, no. 2, pp. 337–349, 2021, doi: 10.24425/aee.2021.136988.
• [9] M. Łukaniszyn, M. Kowol, and J. Kolodziej, “Optimization of a two-phase transverse flux switched reluctance motor with an outer rotor,” Arch. Electr. Eng., vol. 61, no. 4, pp. 567–578, 2012, doi: 0.2478/v10171-012-0042-y.
• [10] M. Akif ̧Sahman, M. Mutluer, and M. Çunka ̧s, “Design optimization of tubular linear voice coil motors using swarm intelligence algorithms,” Eng. Optimiz., vol. 54, no. 22, pp. 1963–1980, 2021, doi: 10.1080/0305215X.2021.1966000.
• [11] Ł. Knypiński, K. Pawełkoszek, and Y. Le Menach, “Optimization of low-power line-start PM motor using gray wolf meta-heuristic algorithm,” Energies, vol. 13, p. 1186, 2020, doi: 10.3390/ en13051186.
• [12] M. Zychlewicz, R. Stanislawski, and M. Kaminski, “Grey Wolf Optimizer in design process of the recurrent wavelet neural controller applied for two-mass system,” Electronics, vol. 11, p. 177, 2022, doi: 10.3390/electronics11020177.
• [13] R. Devarapalli, B. Bhattacharyya, and N.K. Sinha, “An intelligent EGWO-SCA-CS algorithm for PSS parameter tuning under system uncertainties,” Int. J. Intell. Syst., vol. 35, no. 10, pp. 1520–1569, 2020, doi: 10.1002/int.22263.
• [14] S. Duman, H. Kahramanb, Y. Sonmez, U. Guvenc, M. Kati, and S. Aras, “A powerful meta-heuristic search algorithm for solving global optimization and real-world solar photovoltaic parameter estimation problems,” Eng. Appl. Artif. Intell., vol. 111, pp. 1 – 12, 2022, doi: 10.1016/j.engappai.2022.104763.
• [15] A. Brodzicki, M. Piekarski, and J. Jaworek-Korjakowska, “The Whale Optimization Algorithm Approach for Deep Neural Networks,” Sensors, vol. 21, no. 8003, pp. 1 -16, 2021, doi: 10.3390/s21238003.
• [16] J. Ma, Z. Hao, and W. Sun, “Enhancing sparrow search algorithm via multi-strategies for continuous optimization problems,” Inf. Process. Manage., vol. 59, no. 2, p. 102854, 2022, doi: 10.1016/j.ipm.2021.102854.
• [17] A. Burdak, K. Musiał, A. Balashov, A. Batko, and A. Safonyk, “Solving scheduling problems with integrated online sustainability observation using heuristic optimization,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 70, no. 6, p. e143830, 2022, doi: 10.24425/bpasts.2022.143830.
• [18] V. Sarac, “Performance optimization of permanent magnet synchronous motor by cogging torque reduction,” J. Electr. Eng., vol. 70, pp. 218–226, 2019, doi: 10.2478/jee-2019-0030.
• [19] A.N. Patel, “Optimization of power density of axial flux permanent magnet brushless DC motor for electric two-wheeler,” Trends Sci., vol. 18, no. 22, p. 497, 2021, doi: 10.48048/tis.2021.497.
• [20] Y. You and K. Yoon, “Multi-objective optimization of permanent magnet synchronous motor for electric vehicle considering demagnetization,” Appl. Sci., vol. 11, p. 2159, 2021, doi: 10.3390/app11052159.
• [21] M.F. Palangar, A. Mahmoudi, S. Kahourzade, and W.L. Soong, “Simultaneous efficiency and starting torque optimization of a line-start permanent-magnet synchronous motor using two different optimization approaches,” Arab. J. Sci. Eng., vol. 10, pp. 122–130, 2021.
• [22] L. Zaaraoui and A. Mansouri, “Optimization and finite element analysis of an in-wheel permanent magnet motor,” Malays. J. Fundam. Appl. Sci., vol. 17, no. 1, pp. 104–108, 2021, doi: 10.11113/mjfas.v17n1.1981.
• [23] D. Gope and S.K. Goel, “Design optimization of permanent magnet synchronous motor using Taguchi method and experimental validation,” Int. J. Emerg. Electr. Power Syst., vol. 22, no. 1, pp. 9–20, 2021, doi: 10.1515/ijeeps-2020-0169.
• [24] K. Liu, H. Zhang, B. Zhang, and Q. Liu, “Hybrid optimization algorithm based on neural networks and its application in wave-front shaping,” Opt. Express, vol. 29, no. 10, pp. 15517–15527, 2021, doi: 10.1364/OE.424002.
• [25] M. Islam, Y. Gajpal, and T. El-Mekkawy, “Hybrid particle swarm optimization algorithm for solving the clustered vehicle routing problem,” Appl. Soft Comput., vol. 110, pp. 123, 2021, doi: 10.1016/j.asoc.2021.107655.
• [26] Y. Belkourchia, L. Azrar, and E.M. Zeriab, “A hybrid optimization algorithm for solving constrained engineering design problems,” 5th International Conference on Optimization and Applications (ICOA), 2019, pp. 1–6, doi: 10.1109/ICOA.2019.8727654.
• [27] O. Niyomubyeyi, T.E. Sicuaio, J.I. Díaz González, P. Pilesjö, and A. Mansourian, “A comparative study of four metaheuristic algorithms, AMOSA, MOABC, MSPSO, and NSGA-II for evacuation planning,” Algorithms, vol. 13, no. 16, p. 16, 2020, doi: 10.3390/a13010016.
• [28] X.S. Yang and S. Deb, “Cuckoo search via Levy flights,” in Proc. of World Congress on Nature and Biologically Inspired Computing – NaBIC’2009, 2009, doi: 10.1109/NABIC.2009.5393690.
• [29] E. Cuevas and A. Reyna-Orta, “A Cuckoo search algorithm for multimodal optimization,” Sci. World J., vol. 2014, p. 497514, 2014, doi: 10.1155/2014/497514.
• [30] Ł. Knypiński, S. Kuroczycki, and F.P.G. Márquez, “Minimization of Torque Ripple in the Brushless DC Motor Using Constrained Cuckoo search algorithm,” Electronics, vol. 10, p. 2299, 2021, doi: 10.3390/electronics10182299.
• [31] Ł. Knypiński and L. Nowak, “The algorithm of multi-objective optimization of PM synchronous motors,” Przegląd Elektrotechniczny, vol. 95, no. 4, pp. 242–245, 2019, doi: 10.15199/48.2019.04.46.
• [32] Ł. Knypiński, “Performance analysis of selected metaheuristic optimization algorithms applied in the solution of an unconstrained task,” COMPEL, vol. 41, no. 5, pp. 1271–1284, 2022, doi: 10.1108/ COMPEL-07-2021-0254.
• [33] Ł. Knypiński and F. Gillon, “Sizing by optimization of line-start synchronous motor,” Compel-Int. J. Comp. Math. Electr. Electron. Eng., vol. 41, no. 2, pp. 690–702, 2022, doi: 10.1108/COMPEL-06-2021-0221.
• [34] M. Faramarzi Palangar, W. Soong, N. Bianchi, and R. Wang, “Design and optimization techniques in performance improvement of line-start permanent magnet synchronous motors: A review,” IEEE Trans. Magn., vol. 57, no. 9, p. 900214, 2021, doi: 10.1109/TMAG.2021.3098392.
• [35] M. Faramarzi Palangar, A. Mahmoudi, S. Kahourzade, and W. Soong, “Simultaneous efficiency and starting torque optimization of a line-start permanent-magnet synchronous motor using two different optimization approaches,” Arab. J. Sci. Eng., vol. 46, pp. 9953–9964, 2021, doi: 10.1007/s13369-021-05659-8.
• [36] C. Jędryczka, R.M. Wojciechowski, and A. Demenko, “Influence of squirrel cage geometry on the synchronisation of the line start permanent magnet synchronous motor,” IET Sci. Meas.
• Technol., vol. 9, on. 2, pp. 197–203, 2015, doi: 10.1049/iet-smt.2014.0198.
• [37] P. Wasielewski and R. Gradzki, “Standalone brushless motor module optimized for legged robots,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 70, no. 2, p. e141008, 2022, doi: 10.24425/bpasts.2022.141008.
• [38] J. Wang, Y. Li, S. Wu, Z. Yu, and L. Chen, “Analysis of the influence of parameter condition on whole load power factor and efficiency of line start permanent magnet assisted synchronous reluctance motor,” Energies, vol. 15, p. 3866, 2022, doi: 10.3390/en15113866.
• [39] H. Qui, Y. Zhang, K. Hu, C. Yang, and R.Yi, “The influence of stator winding turns on the steady-state performances of line-start permanent magnet synchronous motor,” Energies, vol. 12, p. 2363, 2019, doi: 10.3390/en12122363.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).