Catalogus : Details
 | Simon StockPhysics-informed Machine Learning for Virtual Inertia Provision from Distribution Power Systems | ||||
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| ISBN: | 978-3-8440-9765-8 | ||||
| Reeks: | Schriftenreihe des Instituts für Elektrische Energietechnik Uitgever: Prof. Dr.-Ing. Christian Becker Hamburg | ||||
| Volume: | 5 | ||||
| Trefwoorden: | Power Systems; Renewable Energy sources; Machine Learning | ||||
| Soort publicatie: | Dissertatie | ||||
| Taal: | Engels | ||||
| Pagina's: | 172 pagina's | ||||
| Gewicht: | 225 g | ||||
| Formaat: | 21 x 14,8 cm | ||||
| Bindung: | Softcover | ||||
| Prijs: | 58,80 € / 73,60 SFr | ||||
| Verschijningsdatum: | Januari 2025 | ||||
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| Samenvatting | This thesis develops a framework that enables the provision of Virtual Inertia from power distribution systems. In this way, distributed renewables energies can be utilized to support the overall system frequency. Physics-informed Machine Learning techniques are developed and applied inside this framework. Namely, the Bayesian Physics-informed Neural Network and the Physics-informed Actor Critic. |
