Physics informed neural network-based high-frequency modeling of induction motors
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...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/171868 |
| _version_ | 1824454138057981952 |
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| author | Zhao, Zhenyu Fan, Fei Sun, Quqin Jie, Huamin Shu, Zhou Wang, Wensong See, Kye Yak |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Zhao, Zhenyu Fan, Fei Sun, Quqin Jie, Huamin Shu, Zhou Wang, Wensong See, Kye Yak |
| author_sort | Zhao, Zhenyu |
| collection | NTU |
| description | 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. |
| first_indexed | 2025-02-19T03:17:32Z |
| format | Journal Article |
| id | ntu-10356/171868 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:17:32Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1718682023-11-17T15:41:52Z Physics informed neural network-based high-frequency modeling of induction motors Zhao, Zhenyu Fan, Fei Sun, Quqin Jie, Huamin Shu, Zhou Wang, Wensong See, Kye Yak School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Equivalent Circuit Induction Motor 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. Published version 2023-11-14T01:06:25Z 2023-11-14T01:06:25Z 2022 Journal Article 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 2096-1529 https://hdl.handle.net/10356/171868 10.23919/CJEE.2022.000036 2-s2.0-85147582713 4 8 30 38 en Chinese Journal of Electrical Engineering © 2022 China Machinery Industry Information Institute. This is an open-access article distributed under the terms of the Creative Commons License. application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering Equivalent Circuit Induction Motor Zhao, Zhenyu Fan, Fei Sun, Quqin Jie, Huamin Shu, Zhou Wang, Wensong See, Kye Yak Physics informed neural network-based high-frequency modeling of induction motors |
| title | Physics informed neural network-based high-frequency modeling of induction motors |
| title_full | Physics informed neural network-based high-frequency modeling of induction motors |
| title_fullStr | Physics informed neural network-based high-frequency modeling of induction motors |
| title_full_unstemmed | Physics informed neural network-based high-frequency modeling of induction motors |
| title_short | Physics informed neural network-based high-frequency modeling of induction motors |
| title_sort | physics informed neural network based high frequency modeling of induction motors |
| topic | Engineering::Electrical and electronic engineering Equivalent Circuit Induction Motor |
| url | https://hdl.handle.net/10356/171868 |
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