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- Dokumenttyp:
- Konferenzbeitrag
- Autor(en):
- Rai, A.; Bhujel, N.; Dhiman, V.; Hummels, D.; Tamrakar, U.; Byrne, R.H.; Tonkoski, R.
- Titel:
- A Physics-Informed Neural Network Modeling Approach for Energy Storage-Based Fast Frequency Support in Microgrids
- Stichworte:
- Training; Adaptation models; Perturbation methods; Power system dynamics; Microgrids; Power system stability; Data models; Microgrids; frequency dynamics; physics-informed neural network; modeling
- Kongress- / Buchtitel:
- 2024 IEEE Electrical Energy Storage Application and Technologies Conference (EESAT)
- Datum der Konferenz:
- 15.01.2024 - 17.01.2024
- Verlag / Institution:
- IEEE
- Publikationsdatum:
- 29.01.2024
- Jahr:
- 2024
- Reviewed:
- ja
- Sprache:
- en
- Volltext / DOI:
- doi:10.1109/eesat59125.2024.10471220
- TUM Einrichtung:
- SoED, PT&D (EEN)
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