Development of Digital Twin for DC-DC Converters Under Varying Parameter Conditions

The constantly changing characteristics of sources, loads, and operating environments in microgrids aboard marine vessels warrant the need for the real-time and accurate transient state estimation of the various converters used for power flow management. This paper presents the digital twin development for a parameter-varying non-isolated DC-DC buck (step down) converter to demonstrate the potential of circuit identification and state estimation within a single digital twin model. The digital twin will utilize individual and parameter-specific NARX-RNNs in a centralized model to identify and adapt system state predictions relative to the most current configuration of the buck converter. Additionally, the model’s ability to maintain state estimation accuracy in the presence of circuit component variation will be demonstrated through simulated deviations from nominal values, and model versatility will be shown through testing a simulation-based model on physical hardware. This modular model, which is demonstrated through simulation and experimentation, can be adapted and scaled for additional circuit configurations. It has the potential to be integrated into real-time system monitoring and fault detection systems within multi-converter microgrid environments.