Submodule Reference Voltage Similarity-Based Current Sensor Fault Diagnosis for N-Module PMSM Drive Systems

Abstract

For the N-module permanent magnet synchronous motor (PMSM) drive system, the same phase currents of different submodules can be redundant with each other for closed-loop control. Fast and accurate replacement of currents measured by faulty current sensors is essential for the highly reliable operation of the system. In this article, a similarity learning-based fault diagnosis method for N-module PMSMs is proposed to achieve faster fault detection than traditional methods. Also, the proposed method can accurately identify all 48 fault combinations of current sensors in the faulty submodule. In particular, a hybrid metric distance (HMD) is designed to extract the different characteristics of the coaxial reference voltage based on a modified coordinate transformation of the different axis directions. Based on the HMD, an accumulative sum trigger value (ASTV) method is designed to accurately locate faulty submodules and sensors in case of gain and zero-offset faults. Based on the discrete Laplace operator can accurately locate the faulty submodule and sensor in case of signal loss and stuck faults. In addition, the discrete Laplace operator is combined with the half-wave symmetry variable for fault-type identification. The performance of the proposed method for single-current sensor and dual-current sensor fault diagnosis is experimentally verified in a four-module PMSM driver.