A Machine Learning Perspective for Order Reduction in Electrical Motors modeling

This paper presents two approaches of model order reduction applied to two different type of electrical motors, the Permanent Magnet Synchronous Reluctance Machine (PMASynRM) and the Switched Reluctance Motor(SRM). In the field of Electrical Machines, a motor can be described using a mathematical model, with complex non-linear differential equations, based on equivalent electric circuit parameters (inductances and resistances). Different theoretical and experimental methods have been proposed for estimating the inductances, requiring time consuming tests or simulations. Finding methods to reduce the number of simulations/measurements necessary to compute the parameters of the motors represents a constant concern in the Industry research field. Less measurements/simulations means reducing the computation time, which is a priority in Industry, for a shorter time-to-market. In our experiments we have chosen to reduce the problem dimensions for the computation of the magnetization characteristic of the machines, using Machine Learning. We compared Principal Component Analysis with Polynomial Interpolation and we have reduced the problem space with 50% up to 80%, depending on the motor type and on the context.