Improved LPTN-Based Online Temperature Prediction of Permanent Magnet Machines by Global Parameter Identification

This article presents an improved lumped parameter thermal network (LPTN) based online temperature prediction method of permanent magnet (PM) machines by global parameter identification. The proposed global parameter identification incorporates not only the loss but also the magnetic information int...

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Bibliographic Details
Published inIEEE transactions on industrial electronics (1982) Vol. 70; no. 9; pp. 8830 - 8841
Main Authors Cao, Longfei, Fan, Xinggang, Li, Dawei, Kong, Wubin, Qu, Ronghai, Liu, Zirui
Format Journal Article
LanguageEnglish
Published New York IEEE 01.09.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Convective heat transfer
Couplings
Identification methods
Lumped parameter thermal network (LPTN)
Magnetic domains
Magnetic levitation
Magnetic saturation
online temperature prediction
Parameter estimation
Parameter identification
permanent magnet (PM) machines
Permanent magnets
Skin effect
Temperature
Temperature measurement
Temperature sensors
Thermal analysis
Thermodynamic properties
Torque
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Summary:This article presents an improved lumped parameter thermal network (LPTN) based online temperature prediction method of permanent magnet (PM) machines by global parameter identification. The proposed global parameter identification incorporates not only the loss but also the magnetic information into a fifth-order LPTN and identifies the multiphysical magneto-thermal parameters as a whole. In the identification process, the nonlinear characteristics, including the magnetic saturation, the cross-saturation, the strand skin effect, and the speed-dependent convective heat transfer, are all considered. It can eliminate the cumbersome magnetic and loss measurement efforts and improve the overall identification accuracy compared with the traditional step by step identification method. Based on the identified multiphysical magneto-thermal model, the critical temperatures of the PM machines, together with the online losses and torque, can then be predicted in real time. The experimental results show that the proposed online temperature prediction method can track the real temperature variation with errors within 5 °C.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2022.3208600