Neural Network Aided PMSM Multi-Objective De sign and Optimization for More-Electric Aircraft Applications

This study uses the Neural Network (NN) technique to optimize design of surface-mounted Permanent Magnet Synchronous Motors (PMSMs) for More-Electric Aircraft (MEA) applications. The key role of NN is to provide dedicated correction factors for the analytical PMSM mass and loss estimation within the entire design space. Based on that, a globally optimal design can be quickly obtained. Matching the analytical estimation with finite-element analysis (FEA) is the main research target of training the NN. Conventional analytical formulae serve as the basis of this study, but they are prone to loss accuracy (especially for a large design space) due to their assumptions and simplifications. With the help of the trained NNs, the analytical motor model can give an estimation as accurate as the FEA but with super less time during the optimization process. The Average Correction Factor (ACF) approach is regarded as the comparison method to demonstrate the excellent performance of the proposed NN model. Furthermore, a NN aided three-stage-seven-step optimization methodology is proposed. Finally, a Pole-10-Slot-12 PMSM case study is given to demonstrate the feasibility and gain of the NN aided multi-objective optimization approach. In this case, the NN aided analytical model can generate one motor design in 0.04 secs while it takes more than 1 min for the used FEA model.