Home Journals Articles Updates About
CN
image
收藏切换
Nonlinear Magnetic Flux Identification of Permanent Magnet Synchronous Motors Based on Gaussian Process Regression
收藏切换
Zhongyong LIU1, 2, Tao FAN1, 2, Guolin HE2, Xuhui WEN1, 2
Journal of Power Supply | 2024, 22(3) : 172 - 181
Less
收藏切换
Journal of Power Supply | 2024, 22(3): 172-181
• Gate Driving and Application •
Nonlinear Magnetic Flux Identification of Permanent Magnet Synchronous Motors Based on Gaussian Process Regression
Full
Zhongyong LIU1, 2, Tao FAN1, 2, Guolin HE2, Xuhui WEN1, 2
Affiliations
  • 1 University of Chinese Academy of Sciences Beijing 100049 China
  • 2 Institute of Electrical Engineering, Chinese Academy of Sciences Beijing 100190 China
Published: 2024-05-30 doi: 10.13234/j.issn.2095-2805.2024.3.172
Outline
收藏切换

In the burgeoning field of new energy vehicles, silicon carbide representing a new generation of semi-conductor power devices is progressively replacing silicon-based IGBTs, which also sets higher standards for the motor control performance within the corresponding innovative technological ecosystem. The precision of motor parameters is becoming increasingly critical for enhancing the performance of electric control systems as they evolve from the tradi-tional PI control and direct torque control to advanced algorithms such as model predictive control and neural network control. Aimed at the problem that the classic linear model for permanent magnet synchronous motors cannot adapt to complex and variable conditions due to nonlinear factors such as cross-saturation, a nonlinear magnetic flux identifica-tion method based on Gaussian process regression is proposed. By employing a second-order generalized integrator to acquire the magnetic flux data under dynamic conditions, the system identification is completed. Finally, the effective-ness of the proposed approach and the accuracy of parameter identification were verified through simulation and experi-mental results.

Silicon carbide  /  motor control  /  parameter identification  /  Gaussian process regression
刘忠永, 范涛, 何国林, 温旭辉. 基于高斯过程回归的永磁同步电机非线性磁链辨识. 电源学报, 2024 , 22 (3) : 172 -181 . DOI: 10.13234/j.issn.2095-2805.2024.3.172
Zhongyong LIU, Tao FAN, Guolin HE, Xuhui WEN. Nonlinear Magnetic Flux Identification of Permanent Magnet Synchronous Motors Based on Gaussian Process Regression[J]. Journal of Power Supply, 2024 , 22 (3) : 172 -181 . DOI: 10.13234/j.issn.2095-2805.2024.3.172
Funding
Less
  • National Key Research and Development Program of China(2021YFB2500600)
Appendix
Less
Year 2024 volume 22 Issue 3
1157
529
Cite this Article
BibTeX
Article Info
doi: 10.13234/j.issn.2095-2805.2024.3.172
  • Receive Date：2024-01-31
  • Online Date：2025-07-21
  • Published：2024-05-30
Article Data
Affiliations
History
  • Received：2024-01-31
  • Revised：2024-02-17
  • Accepted：2024-02-21
Funding
National Key Research and Development Program of China(2021YFB2500600)
Affiliations
    1 University of Chinese Academy of Sciences Beijing 100049 China
    2 Institute of Electrical Engineering, Chinese Academy of Sciences Beijing 100190 China
References
Share
https://castjournals.cast.org.cn/joweb/dyxb/EN/10.13234/j.issn.2095-2805.2024.3.172
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House