Synchronous Reluctance Motors (SynRMs) have been widely used in some industrial fields because of their attractive characteristics, such as high efficiency, low cost, and simple structure. In order to reduce the torque ripple of the SynRMs, a non-parametric model is usually used to optimize the rotor structure. However, the conventional method has the problems of the low-accuracy and poor generalization ability. In this paper, an optimization method of the rotor structure is proposed to reduce the torque ripple by utilizing the deep learning algorithm. Firstly, the sample data of the relationship between the rotor structural parameters and torque ripple are obtained with the finite element analysis (FEA). The fast calculation model is established by the deep neural network (DNN). Then, with the goal of not weakening the torque density and minimizing the torque ripple, the immune clone algorithm (ICA) is utilized to optimize the structural parameters of the rotor at different operating points. Finally, the correctness and validity of the method are verified by the simulation analysis. It is concluded that the accuracy of the model established by DNN is acceptable. The proposed method can significantly reduce the torque ripple and increase the torque density.

中文翻译：

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基于深度神经网络模型的同步磁阻电机转子拓扑优化

同步磁阻电动机（SynRMs）由于它们具有高效率，低成本和简单结构等吸引人的特性而被广泛用于某些工业领域。为了减少SynRM的转矩波动，通常使用非参数模型来优化转子结构。但是，传统方法存在精度低，泛化能力差的问题。提出了一种利用深度学习算法降低转子转矩脉动的转子结构优化方法。首先，通过有限元分析（FEA）获得了转子结构参数与转矩脉动之间关系的样本数据。快速计算模型由深度神经网络（DNN）建立。然后，为了不降低转矩密度并使转矩波动最小化，免疫克隆算法（ICA）用于优化转子在不同工作点的结构参数。最后通过仿真分析验证了该方法的正确性和有效性。结论是DNN建立的模型的准确性是可以接受的。所提出的方法可以显着减少转矩脉动并增加转矩密度。