Deep Learning Based Design Methodology for Electric Machines: Data Acquisition, Training and Optimization

نویسندگان

چکیده

This paper presents an automated deep learning-based design methodology to facilitate the and optimization processes in electro-mechanical energy conversion devices. To validate generality of model, a complex machine structure with hybrid Permanent Magnets (PMs) is selected as case study. First, machine’s geometrical topology respective variables are described cylindrical coordinate system programmed into Finite Element (FE) software package. Next, program sweeps through predefined ranges captures corresponding air-gap flux distribution FE-based parametric analysis. The density data post-processed fed neural network (DNN) training algorithm. In particular, 10,000 sets utilized for DNN model. trained model successfully predicts performance any random set parameters, confirmed via FE. Finally, by leveraging structural parameters optimized limit higher-order spatial harmonics cogging torque.

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ژورنال

عنوان ژورنال: IEEE Access

سال: 2023

ISSN: ['2169-3536']

DOI: https://doi.org/10.1109/access.2023.3247011