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Prediction method of motor magnetic field based on improved Linknet model

Liang Jin (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China)
Yuankai Liu (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China)
Qingxin Yang (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China)
Chuang Zhang (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China)
Suzhen Liu (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China)

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering

ISSN: 0332-1649

Article publication date: 26 May 2022

Issue publication date: 12 January 2023

32

Abstract

Purpose

Under the condition of small data set, a prediction model of motor magnetic field is established based on deep learning method. This paper aims to complete the magnetic field prediction quickly and accurately.

Design/methodology/approach

An improved Linknet model is proposed to predict the motor magnetic field. This is a digital twin technology, which can predict the function values of other points according to the function values of typical sampling points. The results of magnetic field distribution are represented by color images. By predicting the pixels of the image, the corresponding magnetic field distribution is obtained. The model not only considers the correlation between pixels but also retains the spatial information in the original input image and can well learn the mapping relationship between motor structure and magnetic field.

Findings

The model can speed up the calculation while ensuring the accuracy and has obvious advantages in large-scale calculation and real-time simulation.

Originality/value

Under the condition of small data set, the model can well learn the mapping relationship between motor structure and magnetic field, so as to complete the magnetic field prediction quickly and accurately. In the future, according to the characteristics of magnetic field distribution, it will lay a foundation for solving the problems of rapid optimization, real-time simulation and physical field control of electrical equipment.

Keywords

Acknowledgements

This article is supported by the major research program (No. 92066206) and general program (No. 51977148) of the National Natural Science Foundation of China (NSFC).

Citation

Jin, L., Liu, Y., Yang, Q., Zhang, C. and Liu, S. (2023), "Prediction method of motor magnetic field based on improved Linknet model", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 42 No. 1, pp. 90-100. https://doi.org/10.1108/COMPEL-02-2022-0081

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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