Michael Schneeberger

Michael Schneeberger
M.Sc.
Michael Schneeberger
PhD Student
Step by step we are able to deal with more sophisticated problems and thereby getting in control of changing needs and requirements.

Michael Schneeberger is a PhD student at the Institute of Electric Power Systems at FHNW supervised by Prof. Silvia Mastellone and Prof. Florian Dörfler. He previously worked at ABB's (later Hitachi's) power grid division as a control software engineer with focus on rail static frequency converters. He holds a Master's degree in electrical and electronic engineering from EPFL.

Scientific Publications

Published
SOS Construction of Compatible Control Lyapunov and Barrier Functions
Proceedings of IFAC World congress

Research projects as Researcher

Title
Principal Investigators

Data-driven control and estimation for the next generation of plug-and-play power converters

Summary

The project aims at developing an autonomous and reconfigurable control architecture where each converter operates in plug-and-play mode, i.e., without additional communication overhead or detailed model knowledge, and under variable system conditions. This requires in a first step that each converter estimates online the equivalent circuit as seen at its terminals. Based on such estimates, the converter controller reconfigures its structure and adapts online its parameters in order to optimally perform under the different grid configurations and operating conditions.

Data-driven control and estimation for the next generation of plug-and-play power converters

The project aims at developing an autonomous and reconfigurable control architecture where each converter operates in plug-and-play mode, i.e., without additional communication overhead or detailed model knowledge, and under variable system conditions. This requires in a first step that each converter estimates online the equivalent circuit as seen at its terminals. Based on such estimates, the converter controller reconfigures its structure and adapts online its parameters in order to optimally perform under the different grid configurations and operating conditions.

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