Neural ODE for Estimation of Flux Linkage Models of Synchronous Machines

Abstract

An accurate estimation of flux linkage maps is essential for the proper control and modeling of synchronous machines. We propose to use neural networks as the flux linkage model with training procedure respecting the differential equation of the stator current. Moreover, the neural network allows straightforward extension of the number of input variables. We demonstrate this ability to estimate the flux linkage as a functionof rotor speed and position modulated by slot harmonics. The proposed approach is demonstrated on real interior permanent magnet synchronous machine data. The results demonstrate a significant improvement in current prediction compared to commonly used methods.