Comparative Analysis of Gaussian Process Regression Modeling of an Induction Machine: Continuous vs. Mixed-Input Approaches

Abstract

This paper investigates the application of machine learning technique for modeling continuous and mixed-input parameters of electrical machines. The design of electrical machines typically requires the consideration of certain parameters as integer values due to their physical significance, including the number of stator/rotor slots, stator wires, and rotor bars. Traditional machine learning methods, which predominantly treat input parameters as purely continuous, may compromise modeling accuracy for such applications. To address this challenge, models capable of handling mixed-input parameters were used for the case study. Two training datasets were generated: one with purely continuous inputs and another with both continuous inputs and a categorical parameter, specifically, the number of stator conductors. Gaussian process regression was employed to build three models: two with continuous kernels, trained on both datasets, and one with a mixed kernel, trained only on the dataset containing a categorical parameter. A comparative analysis, demonstrated on a 1.5 kW induction machine - though applicable to a wide range of machines - illustrates the differences between the proposed approaches. The results highlight the importance of selecting an appropriate model for the Multi- Objective Bayesian optimization of electrical machines.