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Title:

A Physics-Informed Neural Network Modeling Approach for Energy Storage-Based Fast Frequency Support in Microgrids

Document type:
Konferenzbeitrag
Author(s):
Rai, A.; Bhujel, N.; Dhiman, V.; Hummels, D.; Tamrakar, U.; Byrne, R.H.; Tonkoski, R.
Keywords:
Training; Adaptation models; Perturbation methods; Power system dynamics; Microgrids; Power system stability; Data models; Microgrids; frequency dynamics; physics-informed neural network; modeling
Book / Congress title:
2024 IEEE Electrical Energy Storage Application and Technologies Conference (EESAT)
Date of congress:
15.01.2024 - 17.01.2024
Publisher:
IEEE
Date of publication:
29.01.2024
Year:
2024
Reviewed:
ja
Language:
en
Fulltext / DOI:
doi:10.1109/eesat59125.2024.10471220
TUM Institution:
SoED, PT&D (EEN)
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