Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/171868
Title: Physics informed neural network-based high-frequency modeling of induction motors
Authors: Zhao, Zhenyu
Fan, Fei
Sun, Quqin
Jie, Huamin
Shu, Zhou
Wang, Wensong
See, Kye Yak
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Zhao, Z., Fan, F., Sun, Q., Jie, H., Shu, Z., Wang, W. & See, K. Y. (2022). Physics informed neural network-based high-frequency modeling of induction motors. Chinese Journal of Electrical Engineering, 8(4), 30-38. https://dx.doi.org/10.23919/CJEE.2022.000036
Journal: Chinese Journal of Electrical Engineering 
Abstract: The high-frequency (HF) modeling of induction motors plays a key role in predicting the motor terminal overvoltage and conducted emissions in a motor drive system. In this study, a physics informed neural network-based HF modeling method, which has the merits of high accuracy, good versatility, and simple parameterization, is proposed. The proposed model of the induction motor consists of a three-phase equivalent circuit with eighteen circuit elements per phase to ensure model accuracy. The per phase circuit structure is symmetric concerning its phase-start and phase-end points. This symmetry enables the proposed model to be applicable for both star- and delta-connected induction motors without having to recalculate the circuit element values when changing the motor connection from star to delta and vice versa. Motor physics knowledge, namely per-phase impedances, are used in the artificial neural network to obtain the values of the circuit elements. The parameterization can be easily implemented within a few minutes using a common personal computer (PC). Case studies verify the effectiveness of the proposed HF modeling method.
URI: https://hdl.handle.net/10356/171868
ISSN: 2096-1529
DOI: 10.23919/CJEE.2022.000036
Schools: School of Electrical and Electronic Engineering 
Rights: © 2022 China Machinery Industry Information Institute. This is an open-access article distributed under the terms of the Creative Commons License.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

Files in This Item:
File Description SizeFormat 
Physics_Informed_Neural_Network-based_High-frequency_Modeling_of_Induction_Motors.pdf837.16 kBAdobe PDFThumbnail
View/Open

SCOPUSTM   
Citations 50

7
Updated on Jun 9, 2024

Page view(s)

84
Updated on Jun 11, 2024

Download(s)

31
Updated on Jun 11, 2024

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.