Abstract:This paper introduces the deep learning algorithm of artificial intelligence, and establishes the cogging torque prediction analysis method of permanent magnet synchronous motor (PMSM). This lays a foundation for solving the problem of data isolation between motor design, application characteristics and system integration analysis. The relationship between the four structural parameters of PMSM which are air gap, pole embrace, magnet thickness and magnet width and the performance of the cogging torque is selected as the research object. The PMSM is with eight pole-pairs, 48 stator slots. Then, 625 sets of training samples of deep learning about cogging torque are generated by FEM. We build a prediction model with 4 inputs, one output, and 4 hidden layers, which is trained and optimized by using the artificial intelligence deep learning algorithm. The 575 groups in the 625 sets of data are used to train the deep learning prediction model, and the 50 group is used to test the generalization ability of the prediction model. The feasibility of artificial intelligence deep learning prediction model is verified with the FEM.
金亮, 王飞, 杨庆新, 汪冬梅, 寇晓斐. 永磁同步电机性能分析的典型深度学习模型与训练方法[J]. 电工技术学报, 2018, 33(zk1): 41-48.
Jin Liang, Wang Fei, Yang Qingxin, Wang Dongmei, Kou Xiaofei. Typical Deep Learning Model and Training Method for Performance Analysis of Permanent Magnet Synchronous Motor. Transactions of China Electrotechnical Society, 2018, 33(zk1): 41-48.
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