Modelling and optimization of TPMLMs with slotted stators based on Bayesian DNN

Autor: Tao Wu, Peipei Dai, Kai Zhu, Yachao Zhu
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: IET Electric Power Applications, Vol 18, Iss 11, Pp 1679-1689 (2024)
Druh dokumentu: article
ISSN: 1751-8679
1751-8660
DOI: 10.1049/elp2.12504
Popis: Abstract The Permanent Magnet Linear Motor (TPMLM) is widely used in different industrial fields. TPMLMs with slots and iron cores have high power density, but their thrust fluctuations and copper losses are significant. Due to the nonlinearity and saturation of magnetic circuits, their electromagnetic models are complex and the accuracy of numerical methods is very inferior. Substantially accurate modelling is crucial for motor optimisation design. In this paper, a data‐driven modelling method based on Bayesian optimisation deep neural network (DNN) is proposed to improve the accuracy of the electromagnetic field. The finite element (FE) modelling under different structural parameters is analysed and provides a training dataset for DNN. Then, a multi‐objective optimisation problem for the slotted TPMLM is carried out based on the multi‐objective black hole algorithm. Compared to the original design, the average thrust of TPMLM increased by 49.37%, the thrust fluctuation percentage decreased by 9.59%, and the coil copper consumption percentage decreased by 2.64%. The results show that the improved DNN model has very high modelling accuracy, providing a new way for motor design and optimisation.
Databáze: Directory of Open Access Journals
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