EBERHARD WAFFENSCHMIDT

Machine learning algorithms to improve electrical power grids for a carbon free energy supply

In the near future electrical power grids need to supply a number of additional loads and decentralized generated renewable power. This requires an increased effort to control and supervise those dispatchable loads and components. Machine learning algorithms like neural networks can help significantly in these tasks. Examples, which will be presented, are predicting grid states with a drastically reduced effort of measurement equipment, analysing huge amount of measurements for irregularities or locating origins for disturbances in a power grid. Neural networks, which include and consider physical information, are especially well suited for such tasks.

Eberhard Waffenschmidt (Prof. Dr. Ing.) received the degree in electronic engineering and the Ph.D. degree with RWTH Technical University, Aachen, Germany. From 1995 to 2011, he was employed at Philips Research, Aachen, Germany, finally as senior scientist. Since 2011, he is Professor of Electrical Power Grids at TH Köln – University of Applied Sciences, Cologne, Germany. There, he participates the Cologne-Institute for Renewable Energy (CIRE). He is IEEE-member since 2005, meanwhile Senior Member. He is currently Chairman of the Solarenergie-Förderverein Deutschland e.V. (SFV, Society to Promote Solar Energy Germany), Aachen, Germany. His current research interests include identifying and removing obstacles on the way to a 100% use of renewable energy.