Prediction method of motor magnetic field based on improved Linknet model
ISSN: 0332-1649
Article publication date: 26 May 2022
Issue publication date: 12 January 2023
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
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