Vol. 104

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2021-08-03

Fast Backfire Double Annealing Particle Swarm Optimization Algorithm for Parameter Identification of Permanent Magnet Synchronous Motor

By Dingdou Wen, Chuandong Shi, Kaixian Liao, Jianhua Liu, and Yang Zhang
Progress In Electromagnetics Research M, Vol. 104, 23-38, 2021
doi:10.2528/PIERM21052802

Abstract

When particle swarm optimization (PSO) is used to identify the parameters of permanent magnet synchronous motor (PMSM), the movement of particles is not selective, which makes the algorithm easy to fall into the local optimum, and the accuracy is poor. The simulated annealing particle swarm optimization (SAPSO) improves the accuracy and evolution speed, but SAPSO has redundant iteration problems. To solve these problems, a motor parameter identification method based on fast backfire double anneal particle swarm optimization (FBDAPSO) is proposed. By reducing the optimization time and quickly tempering and annealing the "misunderstood" difference, the motor adjustable model and fitness function are designed, and the number of iterations is constantly reset to achieve the effect of online identification. Under different working conditions, simulated and experimental results show that the proposed method can quickly and accurately identify the four parameters of the motor's stator, winding resistance, stator winding d-axis inductance, stator winding q-axis inductance and permanent magnet flux linkage at the same time, compared with the traditional method of parameter identification, and it has better accuracy, rapidity, and robustness.

Citation


Dingdou Wen, Chuandong Shi, Kaixian Liao, Jianhua Liu, and Yang Zhang, "Fast Backfire Double Annealing Particle Swarm Optimization Algorithm for Parameter Identification of Permanent Magnet Synchronous Motor," Progress In Electromagnetics Research M, Vol. 104, 23-38, 2021.
doi:10.2528/PIERM21052802
http://www.jpier.org/PIERM/pier.php?paper=21052802

References


    1. Li, X., et al., "Study of suppression of vibration and noise of PMSM for electric vehicles," IET Electric Power Applications, Vol. 14, No. 7, 1274-1282, 2020.
    doi:10.1049/iet-epa.2019.0805

    2. Yi, P., Z. Sun, and X. Wang, "Research on PMSM harmonic coupling models based on magnetic co-energy," IET Electric Power Applications, Vol. 13, No. 4, 571-579, 2019.
    doi:10.1049/iet-epa.2018.5196

    3. Shi, T., et al., "VSP predictive torque control of PMSM," IET Electric Power Applications, Vol. 13, No. 4, 463-471, 2019.
    doi:10.1049/iet-epa.2018.5497

    4. Chen, Q., et al., "Extension of space-vector-signal-injection-based MTPA control into SVPWM fault-tolerant operation for five-phase IPMSM," IEEE Transactions on Industrial Electronics, Vol. 67, No. 9, 7321-7333, 2020.
    doi:10.1109/TIE.2019.2944066

    5. Du, J., X. Wang, and H. Lv, "Optimization of magnet shape based on efficiency map of IPMSM for EVs," IEEE Transactions on Applied Superconductivity, Vol. 26, No. 7, 1-7, 2016.

    6. Li, L. and Q. Liu, "Research on IPMSM drive system control technology for electric vehicle energy consumption," IEEE Access, Vol. 7, No. 1, 186201-186210, 2019.
    doi:10.1109/ACCESS.2019.2958944

    7. He, C. and T. Wu, "Analysis and design of surface permanent magnet synchronous motor and generator," CES Transactions on Electrical Machines and Systems, Vol. 3, No. 1, 94-100, 2019.
    doi:10.30941/CESTEMS.2019.00013

    8. Perdukova, D., et al., "Dynamic identification of rotor magnetic flux, torque and rotor resistance of induction motor," IEEE Access, Vol. 8, No. 1, 142003-142015, 2020.
    doi:10.1109/ACCESS.2020.3013944

    9. Li, X. and R. Kennel, "General formulation of Kalman-filter-based online parameter identification methods for VSI-fed PMSM," IEEE Transactions on Industrial Electronics, Vol. 68, No. 4, 2856-2864, 2021.
    doi:10.1109/TIE.2020.2977568

    10. Yang, H., et al., "FPGA-based sensorless speed control of PMSM using enhanced performance controller based on the reduced-order EKF," IEEE Journal of Emerging and Selected Topics in Power Electronics, Vol. 9, No. 7, 289-301, 2021.
    doi:10.1109/JESTPE.2019.2962697

    11. Loria, A., E. Panteley, and M. Maghenem, "Strict lyapunov functions for model reference adaptive control: Application to lagrangian systems," IEEE Transactions on Automatic Control, Vol. 64, No. 7, 3040-3045, 2019.
    doi:10.1109/TAC.2018.2874723

    12. Zhao, L., et al., "Second-order sliding-mode observer with online parameter identification for sensorless induction motor drives," IEEE Transactions on Industrial Electronics (1982), Vol. 61, No. 10, 5280-5289, Jan. 1, 2014.
    doi:10.1109/TIE.2014.2301730

    13. Raouti, D., S. Flazi, and D. Benyoucef, "Modeling and identification of electrical parameters of positive DC point-to-plane corona discharge in dry air using RLS method," IEEE Transactions on Plasma Science, Vol. 44, No. 7, 1144-1149, 2016.
    doi:10.1109/TPS.2016.2577634

    14. Zhao, H., et al., "Parameter identification based online noninvasive estimation of rotor temperature in induction motors," IEEE Transactions on Industry Applications, Vol. 57, No. 1, 417-426, 2021.
    doi:10.1109/TIA.2020.3039940

    15. Shao, M., et al., "An improved PSO algorithm for parameter identification of Bouc-Wen model for piezoelectric actuator," Proceedings of the 39th Chinese Control Conference, 1070-1074, Shenyang, China, 2020.

    16. Liu, K. and Z. Q. Zhu, "Position-offset-based parameter estimation using the adaline NN for condition monitoring of permanent-magnet synchronous machines," IEEE Transactions on Industrial Electronics, Vol. 62, No. 4, 2372-2383, 2015.
    doi:10.1109/TIE.2014.2360145

    17. Ortombina, L., et al., "Magnetic model identification of synchronous motors considering speed and load transients," IEEE Transactions on Industry Applications, Vol. 56, No. 5, 4945-4954, 2020.
    doi:10.1109/TIA.2020.3003555

    18. Accetta, A., et al., "GA-based off-line parameter estimation of the induction motor model including magnetic saturation and iron losses," IEEE Open Journal of Industry Applications, Vol. 1, No. 5, 135-147, 2020.
    doi:10.1109/OJIA.2020.3024567

    19. Liu, Z., et al., "GPU implementation of DPSO-RE algorithm for parameters identification of surface PMSM considering VSI nonlinearity," IEEE Journal of Emerging and Selected Topics in Power Electronics, Vol. 5, No. 3, 1334-1345, 2017.
    doi:10.1109/JESTPE.2017.2690688

    20. Liu, Z., et al., "Global identification of electrical and mechanical parameters in PMSM drive based on dynamic self-learning PSO," IEEE Transactions on Power Electronics, Vol. 33, No. 12, 10858-10871, 2018.
    doi:10.1109/TPEL.2018.2801331

    21. Calvini, M., et al., "PSO-based self-commissioning of electrical motor drives," IEEE Transactions on Industrial Electronics, Vol. 62, No. 2, 768-776, 2015.
    doi:10.1109/TIE.2014.2349478

    22. Srivastava, V., et al., "Simulated annealing with variogram-based optimization to quantify spatial patterns of trees extracted from high-resolution images," IEEE Geoscience and Remote Sensing Letters, Vol. 13, No. 8, 1084-1088, 2016.
    doi:10.1109/LGRS.2016.2565743

    23. Pan, X., et al., "Hybrid particle swarm optimization with simulated annealing," Multimedia Tools and Applications, Vol. 78, No. 8, 29921-29936, 2019.
    doi:10.1007/s11042-018-6602-4

    24. Ma, H., et al., "Cooperative adaptive cruise control strategy optimization for electric vehicles based on SA-PSO with model predictive control," IEEE Access, Vol. 8, No. 21, 225745-225756, 2020.
    doi:10.1109/ACCESS.2020.3043370

    25. Wang, J. L., Y. Li, and A. An, "Dynamic parameter identification of upper-limb rehabilitation robot system based on variable parameter particle swarm optimisation," IET Cyber-systems and Robotics, Vol. 2, No. 3, 140-148, Jan. 1, 2020.
    doi:10.1049/iet-csr.2020.0023

    26. Liu, Z., et al., "Parameter estimation for VSI-fed PMSM based on a dynamic PSO with learning strategies," IEEE Transactions on Power Electronics, Vol. 32, No. 4, 3154-3165, 2017.
    doi:10.1109/TPEL.2016.2572186

    27. Sandre-Hernandez, O., et al., "Parameter identification of PMSMs using experimental measurements and a PSO algorithm," IEEE Transactions on Instrumentation and Measurement, Vol. 64, No. 8, 2146-2154, 2015.
    doi:10.1109/TIM.2015.2390958

    28. Dekkers, A. and E. H. L. Aarts, "Global optimization and simulated annealing," Mathematical Programming, Vol. 50, No. 3, 367-393, 1991.
    doi:10.1007/BF01594945