Hannes Wolf

At the Automation and Sensorics in Networked Systems Group, I develop and evaluate machine learning methods for dynamic state estimation and trajectory prediction in electrical power systems. My work focuses on developing and evaluating machine learning architectures, including Neural Ordinary Differential Equations (Neural ODEs), for estimating grid frequency and voltage dynamics from RMS simulation data. My responsibilities include data generation and simulation, model development in Python, management of

At truemetrics, I was responsible for better position estimation for making delivery processes more efficient. I developed, implemented and tested sensor fusion algorithms for combining measurements from inertial measurement units and the global navigation satellite system recorded on smartphones.

At the control systems department, I was assisting current research in the field of microgrid modeling and control. I was responsible for implementing features and automatized tests for the microgrid model, and identifying models for power components.

At the department of electrical drives, I was assisting research on a new soft magnetic material for transversal flux machines. I was responsible for hardware and software development of the test bench, conducting measurements and identifying loss models.

At Schaeffler, I was responsible for developing a temperature estimation algorithm for a permanentmagnet-synchronous-motor. This included model complexity reduction, designing and implementing observers and developing an initialization procedure for the whole thermal model of the powertrain.