Using Machine Learning Technology to Online Predict the Maximum Common Mode Current of Three-phase Motor Drive Inverter

Ximu Zhang; Yang Huang; Jared Walden; Hua Bai; Fanning Jin; Xiaodong Shi; Bing Cheng

doi:10.1109/ECCE47101.2021.9595900

Using Machine Learning Technology to Online Predict the Maximum Common Mode Current of Three-phase Motor Drive Inverter

Ximu Zhang, Yang Huang, Jared Walden, Hua Bai, Fanning Jin, Xiaodong Shi, Bing Cheng

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

To reduce the common-mode voltage (CMV) in the PWM-based motor drive system, many CMV reduction methods have been proposed. However, the performance of such methods has limitations such as only being implemented on particular operating conditions with fixed switching frequency or PWM patterns and relying on the simulation or experimental data. This paper explores machine-learning-based methods to actively evaluate the CM performance. Machine learning methods are employed to actively analyze three popular PWMs (SVPWM, AZSPWM, and DPWMMin) on-chip. In this way, we can online determine the best PWM pattern and switching frequency with a minimum requirement of computation resources based on the torque and speed command.

Original language	English
Title of host publication	2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1373-1379
Number of pages	7
ISBN (Electronic)	9781728151359
DOIs	https://doi.org/10.1109/ECCE47101.2021.9595900
State	Published - 2021
Externally published	Yes
Event	13th IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Virtual, Online, Canada Duration: Oct 10 2021 → Oct 14 2021

Publication series

Name	2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings

Conference

Conference	13th IEEE Energy Conversion Congress and Exposition, ECCE 2021
Country/Territory	Canada
City	Virtual, Online
Period	10/10/21 → 10/14/21

Funding

ACKNOWLEDGMENT This work was funded by Mercedes-Benz R&D North America. The experimental validation made use of the Engineering Research Center Shared Facilities supported by the Engineering Research Center Program of the National Science Foundation and DOE and the CURENT Industry Partnership Program.

Keywords

CMV reduction
machine learning
motor drive system

Access to Document

10.1109/ECCE47101.2021.9595900

Cite this

Zhang, X., Huang, Y., Walden, J., Bai, H., Jin, F., Shi, X., & Cheng, B. (2021). Using Machine Learning Technology to Online Predict the Maximum Common Mode Current of Three-phase Motor Drive Inverter. In 2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings (pp. 1373-1379). (2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ECCE47101.2021.9595900

Zhang, Ximu ; Huang, Yang ; Walden, Jared et al. / Using Machine Learning Technology to Online Predict the Maximum Common Mode Current of Three-phase Motor Drive Inverter. 2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 1373-1379 (2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings).

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title = "Using Machine Learning Technology to Online Predict the Maximum Common Mode Current of Three-phase Motor Drive Inverter",

abstract = "To reduce the common-mode voltage (CMV) in the PWM-based motor drive system, many CMV reduction methods have been proposed. However, the performance of such methods has limitations such as only being implemented on particular operating conditions with fixed switching frequency or PWM patterns and relying on the simulation or experimental data. This paper explores machine-learning-based methods to actively evaluate the CM performance. Machine learning methods are employed to actively analyze three popular PWMs (SVPWM, AZSPWM, and DPWMMin) on-chip. In this way, we can online determine the best PWM pattern and switching frequency with a minimum requirement of computation resources based on the torque and speed command.",

keywords = "CMV reduction, machine learning, motor drive system",

author = "Ximu Zhang and Yang Huang and Jared Walden and Hua Bai and Fanning Jin and Xiaodong Shi and Bing Cheng",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 13th IEEE Energy Conversion Congress and Exposition, ECCE 2021 ; Conference date: 10-10-2021 Through 14-10-2021",

year = "2021",

doi = "10.1109/ECCE47101.2021.9595900",

language = "English",

series = "2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Zhang, X, Huang, Y, Walden, J, Bai, H, Jin, F, Shi, X & Cheng, B 2021, Using Machine Learning Technology to Online Predict the Maximum Common Mode Current of Three-phase Motor Drive Inverter. in 2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings. 2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 1373-1379, 13th IEEE Energy Conversion Congress and Exposition, ECCE 2021, Virtual, Online, Canada, 10/10/21. https://doi.org/10.1109/ECCE47101.2021.9595900

Using Machine Learning Technology to Online Predict the Maximum Common Mode Current of Three-phase Motor Drive Inverter. / Zhang, Ximu; Huang, Yang; Walden, Jared et al.
2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1373-1379 (2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Using Machine Learning Technology to Online Predict the Maximum Common Mode Current of Three-phase Motor Drive Inverter

AU - Zhang, Ximu

AU - Huang, Yang

AU - Walden, Jared

AU - Bai, Hua

AU - Jin, Fanning

AU - Shi, Xiaodong

AU - Cheng, Bing

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N2 - To reduce the common-mode voltage (CMV) in the PWM-based motor drive system, many CMV reduction methods have been proposed. However, the performance of such methods has limitations such as only being implemented on particular operating conditions with fixed switching frequency or PWM patterns and relying on the simulation or experimental data. This paper explores machine-learning-based methods to actively evaluate the CM performance. Machine learning methods are employed to actively analyze three popular PWMs (SVPWM, AZSPWM, and DPWMMin) on-chip. In this way, we can online determine the best PWM pattern and switching frequency with a minimum requirement of computation resources based on the torque and speed command.

AB - To reduce the common-mode voltage (CMV) in the PWM-based motor drive system, many CMV reduction methods have been proposed. However, the performance of such methods has limitations such as only being implemented on particular operating conditions with fixed switching frequency or PWM patterns and relying on the simulation or experimental data. This paper explores machine-learning-based methods to actively evaluate the CM performance. Machine learning methods are employed to actively analyze three popular PWMs (SVPWM, AZSPWM, and DPWMMin) on-chip. In this way, we can online determine the best PWM pattern and switching frequency with a minimum requirement of computation resources based on the torque and speed command.

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Zhang X, Huang Y, Walden J, Bai H, Jin F, Shi X et al. Using Machine Learning Technology to Online Predict the Maximum Common Mode Current of Three-phase Motor Drive Inverter. In 2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. p. 1373-1379. (2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings). doi: 10.1109/ECCE47101.2021.9595900

Using Machine Learning Technology to Online Predict the Maximum Common Mode Current of Three-phase Motor Drive Inverter

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