volume | PIER Journals

Abstract

Aiming at the problems of poor population diversity, slow speed of late identification and low identification accuracy of traditional grey wolf algorithm (GWO), a chaotic adaptive search grey wolf optimization algorithm (CASGWO) for parameter identification of permanent magnet synchronous motor is proposed in this paper. Firstly multiple low-dimensional chaotic mappings are combined; a composite chaotic system Tent-Logistic-Cosine is obtained; uniform populations are generated. So the population diversity and global search capability are improved. Then a segmented nonlinear search method is proposed, where the nonlinear decay factor quickly converges to the vicinity of the optimal solution in the first segment and slows down the convergence rate for local search in the second segment. Thus, the convergence rate is accelerated while the local search capability is enhanced. Finally, the adaptive inertia weights are adjusted according to the fitness values of different wolf pack iterations, and ω wolves approach the leader wolf pack with smaller fitness values at a faster speed. Therefore, the speed of search is again improved, and the local search ability of the algorithm is again enhanced. Experiments show that when identifying multiple parameters of resistance, inductance, and permanent magnet flux of a permanent magnet synchronous motor, the CASGWO method has good global and local search capability, with faster identification speed and higher identification accuracy than the traditional grey wolf algorithm.

1. Fan, Zhong-Xin, Shihua Li, and Rongjie Liu, "ADP-based optimal control for systems with mismatched disturbances: A PMSM application," IEEE Transactions on Circuits and Systems II: Express Briefs, Vol. 70, No. 6, 2057-2061, Jun. 2023.

2. Wu, Lijian and Zekai Lyu, "Harmonic injection-based torque ripple reduction of PMSM with improved DC-link voltage utilization," IEEE Transactions on Power Electronics, Vol. 38, No. 7, 7976-7981, Jul. 2023.

3. Wei, Yao, Fengxiang Wang, Hector Young, Dongliang Ke, and José Rodríguez, "Autoregressive moving average model-free predictive current control for PMSM drives," IEEE Journal of Emerging and Selected Topics in Power Electronics, Vol. 11, No. 4, 3874-3884, Aug. 2023.
doi:10.1109/JESTPE.2023.3275562

4. Chen, Yong, Chunhua Liu, Senyi Liu, and Yuxin Liu, "Predictive control scheme with adaptive overmodulation for a five-leg VSI driving dual PMSMs," IEEE Transactions on Industrial Electronics, Vol. 71, No. 1, 71-81, Jan. 2024.

5. Liu, Jinhao, Jun Yang, Shihua Li, and Xiangyu Wang, "Single-loop robust model predictive speed regulation of PMSM based on exogenous signal preview," IEEE Transactions on Industrial Electronics, Vol. 70, No. 12, 12719-12729, Dec. 2023.
doi:10.1109/TIE.2023.3239938

6. Moon, Hyung-Tae, Hyun-Soo Kim, and Myung-Joong Youn, "A discrete-time predictive current control for PMSM," IEEE Transactions on Power Electronics, Vol. 18, No. 1, 464-472, Jan. 2003.
doi:10.1109/TPEL.2002.807131

7. Zhang, Huixuan, Tao Fan, Liu Meng, Jing Guo, and Xuhui Wen, "Polynomial estimation of flux linkage for predictive current control in PMSM," IEEE Journal of Emerging and Selected Topics in Power Electronics, Vol. 10, No. 5, 6112-6122, Oct. 2022.
doi:10.1109/JESTPE.2022.3173375

8. Wang, Qiwei, Gaolin Wang, Nannan Zhao, Guoqiang Zhang, Qingwen Cui, and Dianguo Xu, "An impedance model-based multiparameter identification method of PMSM for both offline and online conditions," IEEE Transactions on Power Electronics, Vol. 36, No. 1, 727-738, Jan. 2021.
doi:10.1109/TPEL.2020.3000896

9. Grobler, Andries Johannes, Stanley Robert Holm, and George van Schoor, "Empirical parameter identification for a hybrid thermal model of a high-speed permanent magnet synchronous machine," IEEE Transactions on Industrial Electronics, Vol. 65, No. 2, 1616-1625, Feb. 2018.
doi:10.1109/TIE.2017.2733499

10. Rafaq, Muhammad Saad, Francis Mwasilu, Jinuk Kim, Han Ho Choi, and Jin-Woo Jung, "Online parameter identification for model-based sensorless control of interior permanent magnet synchronous machine," IEEE Transactions on Power Electronics, Vol. 32, No. 6, 4631-4643, Jun. 2017.
doi:10.1109/TPEL.2016.2598731

11. Zhu, Zheng, Xiangyu Wang, Bingjie Yan, Liang Li, and Qiong Wu, "A dynamic decoupling control method for PMSM of brake-by-wire system based on parameters estimation," IEEE/ASME Transactions on Mechatronics, Vol. 27, No. 5, 3762-3772, Oct. 2022.
doi:10.1109/TMECH.2021.3130907

12. Liu, Ziyang, Guodong Feng, and Yu Han, "Extended-kalman-filter-based magnet flux linkage and inductance estimation for PMSM considering magnetic saturation," 2021 36th Youth Academic Annual Conference of Chinese Association of Automation (YAC), 430-435, Nanchang, China, 2021.

13. Kivanc, Omer Cihan and Salih Baris Ozturk, "Sensorless PMSM drive based on stator feedforward voltage estimation improved with MRAS multiparameter estimation," IEEE/ASME Transactions on Mechatronics, Vol. 23, No. 3, 1326-1337, Jun. 2018.
doi:10.1109/TMECH.2018.2817246

14. Wu, Zhongqiang and Chunqi Du, "The parameter identification of PMSM based on improved cuckoo algorithm," Neural Processing Letters, Vol. 50, 2701-2715, May 2019.
doi:10.1007/s11063-019-10052-6

15. Zhou, Shuai, Dazhi Wang, and Ye Li, "Parameter identification of permanent magnet synchronous motor based on modified-fuzzy particle swarm optimization," Energy Reports, Vol. 9, No. 1, 873-879, Mar. 2023.
doi:10.1016/j.egyr.2022.11.124

16. Zhang, Xiaoguang, Benshuai Hou, and Yang Mei, "Deadbeat predictive current control of permanent-magnet synchronous motors with stator current and disturbance observer," IEEE Transactions on Power Electronics, Vol. 32, No. 5, 3818-3834, May 2017.
doi:10.1109/TPEL.2016.2592534

17. Liu, Zhao-Hua, Hua-Liang Wei, Qing-Chang Zhong, Kan Liu, and Xiao-Hua Li, "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, Sep. 2017.
doi:10.1109/JESTPE.2017.2690688

18. Xu, Wei, Moustafa Magdi Ismail, Yi Liu, and Md Rabiul Islam, "Parameter optimization of adaptive flux-weakening strategy for permanent-magnet synchronous motor drives based on particle swarm algorithm," IEEE Transactions on Power Electronics, Vol. 34, No. 12, 12128-12140, Dec. 2019.
doi:10.1109/TPEL.2019.2908380

19. Liu, Zhao-Hua, Hua-Liang Wei, Xiao-Hua Li, Kan Liu, and Qing-Chang Zhong, "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, Dec. 2018.
doi:10.1109/TPEL.2018.2801331

20. Singh, Narinder and S. B. Singh, "Hybrid algorithm of particle swarm optimization and grey wolf optimizer for improving convergence performance," Journal of Applied Mathematics, Vol. 2017, 2017.

21. Liu, Hengming and Lu Cao, "Parameter identification of generator excitation system based on improved grey wolf optimization," 2020 4th International Conference on Electrical, Automation and Mechanical Engineering, Vol. 1626, No. 1, 012009, Jun. 2020.
doi:10.1088/1742-6596/1626/1/012009

22. Jiang, Jinmei and Zhu Zhang, "Multi-parameter identification of permanent magnet synchronous motor based on improved grey wolf optimization algorithm," 2021 IEEE 4th Student Conference on Electric Machines and Systems (SCEMS), 1-7, Huzhou, China, 2021.

23. Sun, Xiaodong, Yao Zhang, Xiang Tian, Junhao Cao, and Jianguo Zhu, "Speed sensorless control for IPMSMs using a modified MRAS with gray wolf optimization algorithm," IEEE Transactions on Transportation Electrification, Vol. 8, No. 1, 1326-1337, Mar. 2022.
doi:10.1109/TTE.2021.3093580

24. Mirjalili, S., S. M. Mirjalili, and A. Lewis, "Grey wolf optimizer," Advances in Engineering Software, Vol. 69, 46-61, Mar. 2014.
doi:10.1016/j.advengsoft.2013.12.007

Dr. Yang Zhang

Dr. Ziying Liu

Dr. Mingfeng Zhou

Dr. Sicheng Li

Dr. Jiaxuan Li

Dr. Zhun Cheng