Projekter pr. år
Beskrivelse
This study is utilized for submodule open-circuit fault detection uncertainty analysis of modular multilevel converters. The dataset consists of 8 uncertainty factors and 15 system variables under four operation scenarios. The 1000 sets of uncertainty factor samples are generated randomly as initial configuration of the system. The 15 system variables are obtained by 1000 Monte Carlo simulations. We found that there are 153 residual samples exceeded the threshold of 0.8, which indicated a high false alarm rate.The dataset is related to the article: Y. Liao, Y. Zhang, "Rethinking Model-based Fault Detection: Uncertainties, Risks, and Optimization Based on a Multilevel Converter Case Study," IEEE Transactions on Power Electronics, Accepted in 2024.
Dato for tilgængelighed | 2024 |
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Forlag | IEEE DataPort |
Projekter
- 1 Igangværende
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AI-Power: Artificial Intelligence for Next-Generation Power Electronics
Blaabjerg, F. (PI (principal investigator)), Wang, H. (CoPI), Sahoo, S. (Projektdeltager), Zhao, S. (Projektdeltager), Zhang, Y. (Projektdeltager), Novak, M. (Projektdeltager) & Frøstrup, S. (Projektkoordinator)
01/09/2022 → 31/08/2027
Projekter: Projekt › Forskning
Publikation
- 1 Tidsskriftartikel
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Rethinking Model-based Fault Detection: Uncertainties, Risks, and Optimization based on a Multilevel Converter Case Study
Liao, Y. & Zhang, Y., 2024, I: IEEE Transactions on Power Electronics . 39, 11, s. 14229-14239 11 s., 10608153.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review
Åben adgangFil1 Citationer (Scopus)