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2022-12-22
Decoupling Control of Outer Rotor Coreless Bearingless Permanent Magnet Synchronous Generator Based on Online Least Squares Support Vector Machine Inverse System and Internal Model Controllers
By
Progress In Electromagnetics Research C, Vol. 128, 1-15, 2023
Abstract
An outer rotor coreless bearingless permanent magnet synchronous generator (ORC-BPMSG) is a multivariable, nonlinear, and strongly coupled system. In order to realize the precise control of the ORC-BPMSG, a decoupling control strategy based on online least squares support vector machine (OLS-SVM) inverse system and internal model controllers is proposed. Firstly, on the basis of introducing its operation principle, the mathematical model is established. Secondly, on the basis of analyzing its reversibility, a real-time inverse system of ORC-BPMSG is obtained by using OLS-SVM, and it is connected in series with the original system to form a pseudo-linear system, which realizes the linearization and decoupling of the ORC-BPMSG. Thirdly, the internal model controller is designed to perform closed-loop control of the pseudo-linear system. Finally, the simulated and experimental results show that the proposed control strategy has better stability and decoupling performance than the decoupling control strategy based on the LS-SVM inverse system and PID (Proportion Integral Derivative).
Citation
Huangqiu Zhu, and Liangyu Shen, "Decoupling Control of Outer Rotor Coreless Bearingless Permanent Magnet Synchronous Generator Based on Online Least Squares Support Vector Machine Inverse System and Internal Model Controllers," Progress In Electromagnetics Research C, Vol. 128, 1-15, 2023.
doi:10.2528/PIERC22102105
References

1. Xu, Q., S. Yuan, and X. Liu, "Online detection and location of eccentricity fault in PMSG with external magnetic sensing," IEEE Transactions on Industrial Electronics, Vol. 69, No. 10, 9749-9760, 2022.
doi:10.1109/TIE.2022.3159947

2. Zhang, X., Y. Li, and K. Wang, "Model predictive control of the open-winding PMSG system based on three-dimensional reference voltage-vector," IEEE Transactions on Industrial Electronics, Vol. 67, No. 8, 6312-6322, 2020.
doi:10.1109/TIE.2019.2938478

3. Nian, H. and L. Chen, "Control techniques of open winding PMSG systems fed by integration of three level NPC converters and diode bridges," Chinese Society for Electrical Engineering, Vol. 36, No. 22, 6238-6245, 2016.

4. Koczara, W. and E. Emest, "Smart and decoupled power electronic generation system," Proceeding of IEEE Power Electronics Specialists Conference, 1902-1907, 2004.

5. Ooshima, M., S. Kitazawa, A. Chiba, et al. "Design and analyses of a coreless-stator-type bearingless motor/generator for clean energy generation and storage systems," IEEE Transactions on Magnetics, Vol. 42, No. 10, 3461-3463, 2006.
doi:10.1109/TMAG.2006.879071

6. Ooshima, M., S. Kobayashi, and H. Tanaka, "Magnetic suspension performance of a bearingless motor/generator for flywheel energy storage systems," Proceeding of IEEE PES General Meeting, 1-4, 2010.

7. Diao, X., Y. Hu, H. Zhu, et al. "Bearingless permanent magnet synchronous generator levitation force and electricity generation performance under variable speed and load situation," Journal of Electrical Machinery and Control, Vol. 21, No. 9, 63-72, 2017.

8. Hua, Y., H. Zhu, and Y. Xu, "Multi-objective optimization design of bearingless permanent magnet synchronous generator," IEEE Transactions on Applied Superconductivity, Vol. 30, No. 4, 1-5, 2020.
doi:10.1109/TASC.2020.2970661

9. Liu, B., Y. Zhang, and X. Yan, "Internal model control of doubly fed induction generators based on inverse system method," Power System Technology, Vol. 35, No. 4, 149-153, 2011.

10. Sun, X., L. Chen, H. Jiang, et al. "High-performance control for a bearingless permanent-magnet synchronous motor using neural network inverse scheme plus internal model controllers," IEEE Transactions on Industrial Electronics, Vol. 6, No. 63, 3479-3488, 2016.
doi:10.1109/TIE.2016.2530040

11. Gu, Z. and H. Zhu, "Active disturbance rejection control of 5-degree-of freedom bearingless permanent magnet synchronous motor based on fuzzy neural network inverse system," ISA Transactions, Vol. 101, 1-14, 2020.
doi:10.1016/j.isatra.2019.09.021

12. Zhu, H., L. Cao, Y. Li, et al. "Decoupling control of 5-degree of freedom bearingless synchronous reluctance motor based on least square support vector machine inverse system," Chinese Society for Electrical Engineering, Vol. 33, No. 15, 99-108, 2013.

13. Liu, G., Y. Zhang, H. Wei, et al. "Least squares support vector machines inverse control for two-motor variable frequency speed-regulating system based on active disturbances rejection," Chinese Society for Electrical Engineering, Vol. 32, No. 6, 138-144, 2012.

14. Liu, G., L. Chen, W. Zhao, et al. "Internal model control of permanent magnet synchronous motor using support vector machine generalized inverse," IEEE Transactions on Industrial Informatics, Vol. 9, No. 2, 890-899, 2013.
doi:10.1109/TII.2012.2222652

15. Xing, J., R. Wang, Q. Yang, et al. "Online training algorithm research based on improved weighed LSSVM," Proceeding of Chinese Control Conference, 5055-5060, 2010.

16. Liu, B. and X. Cheng, "An incremental algorithm of support vector machine based on distance and K nearest neighbor," Proceeding of IEEE International Conference on Computer Science and Automation Engineering, 18-20, 2011.

17. Wong, P., Q. Xu, C. Vong, et al. "Rate-dependent hysteresis modeling and control of a piezo stage using online support vector machine and relevance vector machine," IEEE Transactions on Industrial Electronics, Vol. 59, No. 4, 1988-2001, 2011.
doi:10.1109/TIE.2011.2166235

18. Xu, B. and H. Zhu, "The parameters of LS-SVM are optimized by improved genetic algorithm and improved particle swarm optimization algorithm to improve the performance of LS-SVM, thus improving the fitting accuracy of the inverse system," IEEE Transactions on Industrial Electronics, Vol. 69, No. 12, 12182-12190, 2022.
doi:10.1109/TIE.2021.3130345

19. Zhu, H. and T. Liu, "Rotor displacement self-sensing modeling of six-pole radial hybrid magnetic bearing using improved particle swarm optimization support vector machine," IEEE Transactions on Industrial Electronics, Vol. 35, No. 11, 12296-12306, 2020.

20. Hu, J., M. Wu, X. Chen, et al. "A multilevel prediction model of carbon efficiency based on the differential evolution algorithm for the iron ore sintering process," IEEE Transactions on Industrial Electronics, Vol. 65, No. 11, 8778-8787, 2018.
doi:10.1109/TIE.2018.2811371