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Technical University of Munich

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Machine Learning for Power Systems

19.07.2022, Diplomarbeiten, Bachelor- und Masterarbeiten

We are offering Master's or Bachelor's thesis topics exploring the advanced use of machine learning for the control of power systems.

Electricity is the foundation of modern society. To deliver electrical power reliably is therefore one of the most central technical challenges of our time. However, traditional methods of power system control are being brought to their limits recently.
Firstly, the traditional top-down topology of power grids is increasingly transforming into more distributed systems. Electric vehicles can be seen as both high power consumers and flexible energy storage. At the same time, the supply responsibility of large scale power plants is increasingly shared by small scale power generation.
Secondly, the adoption of large scale renewable energy sources means a substantial increase in generation volatility. Overall, the new generation of power system controllers must be able to deal with a high degree of topological complexity and considerable uncertainty.

Data-driven reinforcement learning controllers are promising candidates because of their ability to learn without precise model knowledge, their on-line efficiency, and their resilience against unknown situations. Quite some research has been done in this area, yet the field is still rapidly growing. The target of the thesis will be to explore the advanced use of machine learning for the control of power systems.

Please apply via our application portal

Kontakt: michael.eichelbeck@tum.de

More Information

2022_Thesis_Proposal_Machine_Learning_for_Power_Systems 2022_Thesis_Proposal_Machine_Learning_for_Power_Systems, (Type: application/pdf, Size: 132.5 kB) Save attachment

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