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.
eBook - PDF
Physics-informed Machine Learning for Virtual Inertia Provision from Distribution Power Systems
- 172 pages
- English
- PDF
- Available on iOS & Android
eBook - PDF
Physics-informed Machine Learning for Virtual Inertia Provision from Distribution Power Systems
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Table of contents
- Introduction
- Frequency Dynamics of Power Systems
- Physics-informed Machine Learning
- Inertia Support Framework
- Estimation of System Inertia
- Coordination of Virtual Inertia Provision
- Discussion
- Conclusions and outlook
- Appendix
- Bibliography