[Oral Presentation]Thermal Modeling of Tubular Permanent Magnet Linear Synchronous Motor Based on Random Forest

IMPORTANT DATES

Deadline for digest submission: Jan, 31, 2021
Notification of acceptance: Mar, 10, 2021
Full paper submission: May, 31, 2021

Thermal Modeling of Tubular Permanent Magnet Linear Synchronous Motor Based on Random Forest

ID：111 Submission ID：111 View Protection：ATTENDEE Updated Time：2021-06-19 16:59:00 Hits：910 Oral Presentation

Start Time：2021-07-02 10:30 （Asia/Shanghai）

Duration：20min

Session：[S1] Concurrent Session 1 » [S1-3] Oral Session 11 & 14

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Abstract

This paper proposed a novel thermal modeling analysis method of tubular permanent magnet linear synchronous motor(PMLSM) based on machine-leaning method.Firstly, the structure and main parameters and the finite element (FE) thermal modeling of motor are introduced.A small sample about the average temperature rise of permanent magnet, overall average temperature rise of PMLSM and coil temperature rise are obtained by FEM method, corresponding to different heat source inputs. Based on the sample dataset, a powerful machine learning algorithm called Random Forest(RF) is employed to fit the function relationship between output design objectives and input sources parameters. The accuracy of thermal prediction model is verified by the remaining group of sample data. Comprehensive performance comparison shows that the motor thermal prediction model was established by RF is better than artificial neural network(ANN).

Keywords

Tubular Permanent Magnet Linear Synchronous Motor，Random Forest，Temperature Field Modeling

Speaker

Tao Wu

School of automation； China University of Geosciences (Wuhan)

Tao Wu (M’19) received the B.E. degree and the M.S. degree from the China University of Geoscience, Hubei, China, in 2001 and 2004, respectively; and the Ph.D. degree in motors and electrical appliances from the Huazhong University of Science and Technology, Wuhan, China, in 2010.
He is currently an associate professor with the School of Automation, China University of Geoscience. His current research interests include motors and controls, design and optimization of electrical systems, and equipment and instruments.

Submission Author

Tao Wu School of Automation; China University of Geosciences (Wuhan)

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