Research on High-Frequency Vibration Reduction for Active Magnetic Bearings With Carrier Phase Shifting PWM

Abstract

The reduction of vibration and noise in active magnetic bearings (AMBs) within electric motors has garnered increasing attention. However, there has been limited research on the high-frequency (HF) vibration introduced by AMB controllers in relation to the switching frequency. In this article, we pioneer an exploration into the mechanism and characteristics of HF vibration in magnetic bearings. We introduce a carrier phase-shifting (CPS) method to analyze its impact on the spatial distribution of electromagnetic force in the air gap and vibration response. Subsequently, we construct a deep neural network (DNN) to model and predict the actual vibration of magnetic bearings, and integrate the grey wolf optimization (GWO) algorithm to optimize the CPS mode, phase-shifting angles, and switching frequency. Finally, experimental validation confirms the effectiveness of our proposed CPS method and artificial intelligence (AI) optimization algorithm. Our results demonstrate a remarkable reduction in HF vibration of AMB, achieving a maximum reduction of 90% and an average of 84% through intelligent optimization.