  In this paper, a hyperparameter tuning based Bayesian optimization of digital
twins is carried out to diagnose various faults in grid connected inverters. As
fault detection and diagnosis require very high precision, we channelize our
efforts towards an online optimization of the digital twins, which, in turn,
allows a flexible implementation with limited amount of data. As a result, the
proposed framework not only becomes a practical solution for model versioning
and deployment of digital twins design with limited data, but also allows
integration of deep learning tools to improve the hyperparameter tuning
capabilities. For classification performance assessment, we consider different
fault cases in virtual synchronous generator (VSG) controlled grid-forming
converters and demonstrate the efficacy of our approach. Our research outcomes
reveal the increased accuracy and fidelity levels achieved by our digital twin
design, overcoming the shortcomings of traditional hyperparameter tuning
methods.
