 | Simon StockPhysics-informed Machine Learning for Virtual Inertia Provision from Distribution Power Systems |
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 |  | | voorkant | achterkant |
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| ISBN: | 978-3-8440-9765-8 |
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| Reeks: | Schriftenreihe des Instituts für Elektrische Energietechnik |
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| Volume: | 5 |
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| Trefwoorden: | Power Systems; Renewable Energy sources; Machine Learning |
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| Soort publicatie: | Dissertatie |
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| Taal: | Engels |
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| Pagina's: | 172 pagina's |
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| Gewicht: | 254 g |
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| Formaat: | 21 x 14,8 cm |
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| Bindung: | Softcover |
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| Prijs: | 58,80 € / 73,60 SFr |
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| 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. |
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