Current Harmonics Minimization of Permanent Magnet Synchronous Machine Based on Iterative Learning Control and Neural Networks

Abstract

Electrical machines generate unwanted flux and current harmonics. Harmonics can be suppressed using various methods. In this paper, the harmonics are significantly reduced using Iterative Learning Control (ILC) and Neural Networks (NNs). The ILC can compensate for the harmonics well for operation at constant speed and current reference values. The NNs are trained with the data from the ILC and help to suppress the harmonics well even in transient operation. The simulation model is based on flux and torque maps, depending on dq-currents and the electrical angle. The maps are generated from FEM simulation of an interior permanent magnet synchronous machine (IPM) and are published with the paper. They are intended to serve other researchers for direct comparison with their own methods. Simulation results in this paper verify that by using ILC and NNs together, current harmonics in transient operation can be eliminated better than without NNs.