Physics-informed Machine Learning for Virtual Inertia Provision from Distribution Power Systems

Beschreibung

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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Simon Stock

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Power Systems Renewable Energy sources Machine Learning

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