This article proposes a digital twin (DT)-based diagnosis and fault-tolerant control for T-type three-level rectifiers. To develop the DT, a dense deep neural network (DNN) machine learning approach is used. The DT is trained offline using a set of experimental data and updated online to get the maximum possible accuracy. Then, the DT is used for the diagnosis and tolerance of open-switch faults (OSFs) and faults related to voltage and current sensors or for sensorless control. The OSF detection and localization algorithm is implemented based on the dynamic response difference between the physical system and its DT. First, the OSF is detected and localized based on the grid current dynamics, where each switch fault generates a specific pattern in the current dynamics. OSF is tolerated by changing the switching function based on the location of the fault. Second, the voltage and current sensor fault is detected when the DT provides a specific amplitude of currents while the physical sensors do not provide a correct measurement. This case is tolerated by feeding back the grid currents or voltages from the DT as an alternative to the physical sensors. The proposed technique has low overhead, enhances the reliability of the power converter, and is applicable for sensorless mode of control. Experimental investigations are conducted to validate the proposed concept.

中文翻译：

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基于数字孪生的T型三电平整流器诊断与容差控制

本文提出了一种基于数字孪生（DT）的T型三电平整流器诊断和容错控制。为了开发 DT，使用了密集深度神经网络 (DNN) 机器学习方法。DT 使​​用一组实验数据进行离线训练，并在线更新以获得最大可能的准确性。然后，DT 用于开路开关故障 (OSF) 以及与电压和电流传感器相关的故障或无传感器控制的诊断和容错。OSF检测和定位算法是基于物理系统与其DT之间的动态响应差异来实现的。首先，根据电网电流动态检测和定位 OSF，其中每个开关故障都会在电流动态中生成特定模式。通过根据故障位置改变切换功能来容忍 OSF。其次，当 DT 提供特定电流幅度而物理传感器未提供正确的测量值时，会检测到电压和电流传感器故障。通过从 DT 反馈电网电流或电压作为物理传感器的替代方案，可以容忍这种情况。所提出的技术开销低，增强了功率转换器的可靠性，并且适用于无传感器控制模式。进行实验研究以验证所提出的概念。通过从 DT 反馈电网电流或电压作为物理传感器的替代方案，可以容忍这种情况。所提出的技术开销低，增强了功率转换器的可靠性，并且适用于无传感器控制模式。进行实验研究以验证所提出的概念。通过从 DT 反馈电网电流或电压作为物理传感器的替代方案，可以容忍这种情况。所提出的技术开销低，增强了功率转换器的可靠性，并且适用于无传感器控制模式。进行实验研究以验证所提出的概念。