Performance analysis and optimization of permanent magnet synchronous motor based on deep learning

In this paper, a method of deep learning is built to reduce the needed time on performance analyze and optimization of permanent magnet synchronous motor (PMSM). The analysis of the electromagnetic speed, torque and efficiency of PMSM is carried on with Finite Element Method (FEM), which is 8 pole-pairs, 48 stator slots and 195mm of stator external diameter. FEM model of PMSM is established, and the finite element analysis is carried out to obtain the structural parameters which have great influence on the maximum efficiency of permanent magnet synchronous motor. Then, the training samples of deep learning about efficiency are generated by FEM. We build a multiple regression model with 3 hidden layers, two inputs, and one output, which is trained and optimized by using the deep learning neural network algorithm. The accuracy of the model is verified by the comparison of the finite element calculation and the multiple regression prediction model fitting results.