A High-Precision Position Estimation Method for Low Saliency Ratio Machine Based on Convolutional Neural Networks

Abstract

Sensorless control of surface permanent magnet synchronous motor (SPMSM) faces great challenges in achieving high precision of rotor position estimation at low speed because the saliency of an SPMSM is only a minor secondary effect. In order to extract accurate position information for SPMSM with low and ultra-low saliency ratios, this paper presents a method that implements image recognition techniques with high-frequency (HF) rotating voltage vector injection. For building an image of the induced current vector, the information of the negative-sequence and the second harmonic of positive-sequence HF current signals in the stationary reference frame is extracted. Then, convolutional neural networks (CNN) are applied to the current vector images to establish the relationship between the vector image and the rotor position. Subsequently, the rotor position can be obtained from the result of image recognition from the neural network. Benefiting from CNN’s excellent performance in image classification, the proposed method is able to recognize subtle position signatures even under low saliency. This confirms the opportunity to increase position estimation accuracy for sensorless control of SPMSM. Finally, the experimental results have provided proof that the proposed method has significantly enhanced estimation accuracy.