Master Thesis Application of Physics-Informed Machine Learning for Grid-Tied Inverter Control

• As a part of your Master thesis, you will develop a physics-informed current controller for grid-tied inverter control.
• You will evaluate the linear regulator and the predictive controller as reference points, as well as establish their boundaries.
• In addition, you will research current AI-based implementations in the literature.
• Furthermore, you will be responsible for developing a concept to improve the implementation of AI-based black boxes and mitigate their drawbacks in grid-tied inverter applications.
• Last but not least, you will select an AI-based concept, which may include machine learning, recurrent neural networks or reinforcement learning. If necessary, you will model the system alongside the implemented controller.

• Education: Master studies in the field of Engineering, Mathematics, Information Technology or comparable
• Experience and Knowledge: in the application of AI methods, system modelling and control engineering; in power electronics advantageous
• Personality and Working Practice: you are a team player with good communication skills who is able to work independently, proactively and meticulously
• Work Routine: office and laboratory work required, partially mobile working possible
• Languages: good in English

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