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Title:

Neural Network-Based Dynamic State Estimation for Fast Frequency Support Using Energy Storage Systems

Document type:
Konferenzbeitrag
Contribution type:
Textbeitrag / Aufsatz
Author(s):
Bhujel, N.; Rai, A.; Hummels, D.; Tamrakar, U.; Tonkoski, R.
Keywords:
Adaptation models; System dynamics; Computational modeling; Microgrids; Frequency estimation; Data models; Velocity measurement; state estimator; neural network; frequency dynamics; microgrids; fast frequency support
Book / Congress title:
2024 IEEE Electrical Energy Storage Application and Technologies Conference (EESAT)
Publisher:
IEEE
Date of publication:
29.01.2024
Year:
2024
Reviewed:
ja
Language:
en
Fulltext / DOI:
doi:10.1109/eesat59125.2024.10471218
TUM Institution:
SoED, PT&D (EEN)
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