Electrical machines surrogate‐based design optimization based on novel waveform targeting strategy with improvement of the computational efficiency

Abstract

Electrical machine design optimization is an expensive procedure as it contains numerous variables and multiple objectives. Therefore, it might require hundreds of time-consuming finite element analyses (FEA). Surrogate models are one of the superior alternatives that can overcome the computational burden of FEA. However, in optimization problems with sensitive input-output relationships, surrogate models can lack accuracy or suffer from unreasonable initial FE-computational cost. To tackle this problem, this work presents a novel strategy called Waveform-Targeted Surrogate Modeling (WTSM) that improves the computational efficiency of surrogate-based design optimization of electrical machines by modifying the model construction process. In this paper, an Interior Permanent Magnet Machine (IPMSM) is studied to evaluate the proposed strategy's performance compared to the conventional Black Box Modeling (BBM) method. A two-step assessment procedure consisting of multiple scenarios has been developed to analyse the reliability of the WTSM concerning different training-validation datasets. Meanwhile, the Kriging model and Latin-Hypercube-Sampling (LHS) method are employed for surrogate model construction purposes. From the discussion and the results, it can be found that the proposed WTSM strategy can significantly increase the accuracy of the surrogate modelling procedure while the required computational cost can be reduced.