Journal
2022 IEEE ENERGY CONVERSION CONGRESS AND EXPOSITION (ECCE)
Volume -, Issue -, Pages -Publisher
IEEE
DOI: 10.1109/ECCE50734.2022.9947986
Keywords
digital twin; grid connected inverters; fault diagnosis; hyperparameter tuning; LightGBM; deep learning
Funding
- FIREMAN project - Spanish national foundation [CHIST-ERA-17-BDSI-003, PCI2019-103780]
- Academy of Finland (AoF) [326270]
- EnergyNet Research Fellowship [321265/328869/352654]
- X-SDEN project [349965]
- Nordic Energy Research via Next-uGrid project [117766]
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This study conducts hyperparameter tuning based Bayesian optimization of digital twins to diagnose faults in grid connected inverters, demonstrating improved accuracy and flexibility in digital twin design.
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.
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