Three-phase PWM voltage-source rectifier (VSR) systems have been widely used in various energy conversion systems, where current sensors are the key component for state monitoring and system control. The current sensor faults may bring hidden danger or damage to the whole system; therefore, this paper proposed a random forest (RF) and current fault texture feature-based method for current sensor fault diagnosis in three-phase PWM VSR systems. First, the three-phase alternating currents (ACs) of the three-phase PWM VSR are collected to extract the current fault texture features, and no additional hardware sensors are needed to avoid causing additional unstable factors. Then, the current fault texture features are adopted to train the random forest current sensor fault detection and diagnosis (CSFDD) classifier, which is a data-driven CSFDD classifier. Finally, the effectiveness of the proposed method is verified by simulation experiments. The result shows that the current sensor faults can be detected and located successfully and that it can effectively provide fault locations for maintenance personnel to keep the stable operation of the whole system.

该论文提出了一种基于随机森林和电流故障纹理特征的方法，用于对三相PWM电压源整流器系统中电流传感器故障进行诊断。结果表明，该方法能够成功地检测和定位电流传感器故障，有效地为维护人员提供故障位置，以保持整个系统的稳定运行。

一种基于随机森林和电流故障纹理特征的三相PWM VSR电流传感器故障诊断方法