TY - GEN
T1 - Data-Driven Based Hybrid Predictive Model for the PMSM Drive System
AU - Wang, Taoming
AU - Wang, Jing
AU - Guan, Wenqing
AU - Liu, Chunqiang
AU - Chen, Yifei
AU - Chen, Zhe
AU - Luo, Guangzhao
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Permanent magnet synchronous motor (PMSM) drive system is a time-varying nonlinear system that integrates several physical domains, including mechanical, electrical, and electromagnetic. Thus, obtaining an accurate mathematical model of PMSM drive system is an easily overlooked challenge. In this paper, a data-driven based machine learning approach is introduced to model the dynamics of PMSM drive system. Compared to traditional mathematical PMSM model, it does not include initial parameters and any assumptions. In this paper, a time series datasets of the drive system are constructed for the whole operating range of the PMSM. And then, the Pearson correlation is adopted to investigate the coupling between variables of PMSM states. To predict the PMSM states, a hybrid predictive models based on the long-short term memory and transformer are proposed. The data of dq-axis currents, speed and electromagnet torque can be obtained by feeding the data of voltage variables into the hybrid predictive models. Finally, the test results show that the proposed hybrid predictive model can accurately predict the temporal dynamics of PMSM drive system in real time.
AB - Permanent magnet synchronous motor (PMSM) drive system is a time-varying nonlinear system that integrates several physical domains, including mechanical, electrical, and electromagnetic. Thus, obtaining an accurate mathematical model of PMSM drive system is an easily overlooked challenge. In this paper, a data-driven based machine learning approach is introduced to model the dynamics of PMSM drive system. Compared to traditional mathematical PMSM model, it does not include initial parameters and any assumptions. In this paper, a time series datasets of the drive system are constructed for the whole operating range of the PMSM. And then, the Pearson correlation is adopted to investigate the coupling between variables of PMSM states. To predict the PMSM states, a hybrid predictive models based on the long-short term memory and transformer are proposed. The data of dq-axis currents, speed and electromagnet torque can be obtained by feeding the data of voltage variables into the hybrid predictive models. Finally, the test results show that the proposed hybrid predictive model can accurately predict the temporal dynamics of PMSM drive system in real time.
KW - machine learning
KW - permanent magnet synchronous motor (PMSM)
KW - variable speed drive
UR - http://www.scopus.com/inward/record.url?scp=85166206369&partnerID=8YFLogxK
U2 - 10.1109/PRECEDE57319.2023.10174328
DO - 10.1109/PRECEDE57319.2023.10174328
M3 - 会议稿件
AN - SCOPUS:85166206369
T3 - 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023
BT - 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics, PRECEDE 2023
Y2 - 16 June 2023 through 19 June 2023
ER -