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Prediction method of motor magnetic field based on improved Linknet model

Prediction method of motor magnetic field based on improved Linknet model 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/approachAn 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.FindingsThe model can speed up the calculation while ensuring the accuracy and has obvious advantages in large-scale calculation and real-time simulation.Originality/valueUnder 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering Emerald Publishing

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References (17)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0332-1649
eISSN
0332-1649
DOI
10.1108/compel-02-2022-0081
Publisher site
See Article on Publisher Site

Abstract

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/approachAn 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.FindingsThe model can speed up the calculation while ensuring the accuracy and has obvious advantages in large-scale calculation and real-time simulation.Originality/valueUnder 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.

Journal

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic EngineeringEmerald Publishing

Published: Jan 12, 2023

Keywords: Finite element method (FEM); Deep learning (DL); Linknet; Motor magnetic field; Electrical machine; Electromagnetic fields

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