论文标题
三相PWM VSR中电流传感器故障诊断的随机森林和当前故障纹理特征的方法
A Random Forest and Current Fault Texture Feature-Based Method for Current Sensor Fault Diagnosis in Three-Phase PWM VSR
论文作者
论文摘要
三相PWM电压源整流器（VSR）系统已被广泛用于各种能量转换系统，当前传感器是状态监视和系统控制的关键组件。当前的传感器故障可能会对整个系统造成隐藏的危险或损坏；因此，本文提出了一个随机森林（RF）和当前的故障纹理特征方法，用于三相PWM VSR系统中的当前传感器故障诊断。首先，收集了三相PWM VSR的三相交流电流（AC）以提取当前的故障纹理特征，并且不需要其他硬件传感器来避免引起其他不稳定的因素。然后，采用当前的故障纹理特征来训练随机森林当前传感器故障检测和诊断（CSFDD）分类器，该分类器是数据驱动的CSFDD分类器。最后，通过模拟实验验证了所提出方法的有效性。结果表明，当前的传感器故障可以被检测到并取得成功，并且可以有效地为维护人员提供故障位置，以保持整个系统的稳定操作。
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.
