Interturn Fault Diagnosis for Model-Predictive-Controlled-PMSM Based on Cost Function and Wavelet Transform

Abstract

Interturn fault (ITF) is a common fault for permanent magnet synchronous machine (PMSM). If the ITF is not detected in time, its propagation may result in the secondary damage, even the damage to the whole electrical machine. Recently, model predictive control (MPC) has been widely studied for the PMSM drive system because of its simple structure and excellent dynamic performance. However, the previous ITF diagnosis methods are mainly focused on vector control and direct toque control systems, and few research works are specifically focused on ITF diagnosis for the model-predictive-controlled-PMSM. Hence, to fill this gap and improve the reliability of the model-predictive-controlled-PMSM, this article first proposes an ITF diagnosis method for the model-predictive-controlled-PMSM, taking into account the characteristics of the MPC system. In this method, wavelet transform is applied to extract the fault feature from the cost function existing in the MPC system, and the ITF is diagnosed by monitoring the normalized energy-related feature vector calculated from the wavelet transform coefficients. Additionally, to further show the performance of the proposed method, the popular diagnosis methods based on the stator current and zero sequence voltage component are studied for the comparative analysis. Both the simulation and experimental results verify the effectiveness of the proposed fault diagnosis method.