Digital-Twin-Based Diagnosis and Tolerant Control of T-Type Three-Level Rectifiers

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.