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- Dokumenttyp:
- Konferenzbeitrag
- Art des Konferenzbeitrags:
- Textbeitrag / Aufsatz
- Autor(en):
- Bhujel, N.; Rai, A.; Hummels, D.; Tamrakar, U.; Tonkoski, R.
- Titel:
- Neural Network-Based Dynamic State Estimation for Fast Frequency Support Using Energy Storage Systems
- Stichworte:
- Adaptation models; System dynamics; Computational modeling; Microgrids; Frequency estimation; Data models; Velocity measurement; state estimator; neural network; frequency dynamics; microgrids; fast frequency support
- Kongress- / Buchtitel:
- 2024 IEEE Electrical Energy Storage Application and Technologies Conference (EESAT)
- Verlag / Institution:
- IEEE
- Publikationsdatum:
- 29.01.2024
- Jahr:
- 2024
- Reviewed:
- ja
- Sprache:
- en
- Volltext / DOI:
- doi:10.1109/eesat59125.2024.10471218
- TUM Einrichtung:
- SoED, PT&D (EEN)
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