Tasks:
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Develop and evaluate reinforcement learning (RL) strategies to coordinate protection elements (e.g., circuit breakers, relays) in high-voltage transmission grids.
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Design grid scenarios (e.g., multi-faults, communication delays, islanding) and simulate them using synthetic fault data.
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Train RL agents to minimize fault impact and improve restoration behavior.
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Analyze robustness under different operating conditions and topologies.
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(Optional) Investigate hybrid RL + rule-based schemes or curriculum learning.
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(Optional) Contribute to a research publication based on your results.
Requirements:
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Solid experience with PyTorch
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Experience training deep learning models
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Ability to attend in-person meetings
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Bonus: Background in electrical engineering, power systems, or control theory
Application:
Send your application with the subject
“Application RL Protection Thesis + your full name” to julian.oelhaf@fau.de and include:
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Curriculum Vitae (CV)
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Short motivation letter (max. one page)
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Transcript of records
This topic can also be conducted as a smaller project (e.g., research or programming project) instead of a full thesis.