SEMINAR: Learning and Control from Fluctuations in Distribution Grids: Graphs, Buildings and MDPs
Distribution Networks provide the final tier in the transfer of electricity from generators to the end consumers. In recent years, smart controllable devices, residential generator/storage devices and distribution grid meters have expanded the availability of sensor data in distribution networks. We discuss learning and control problems in distribution grid using available time-series measurements. For a range of realistic operating conditions, machine learning algorithms based on the dynamics of power flows are proposed to learn the structure of the distribution network as well as to estimate the statistics of load profiles at missing nodes and/or line parameters. The learning methods are generalizable for estimation of in other network of interest like sensor networks and smart buildings. We also discuss a Markov Decision Process framework for network aware control of smart devices in distribution grids under uncertainty that co-optimizes user comfort and global objectives.
Ph.D. Researcher, Los Alamos National Laboratory
Deepjyoti Deka is a post-doctoral researcher in the Theory Division at Los Alamos National Laboratory, USA. He received the M.S. and Ph.D. degrees in Electrical Engineering from University of Texas, Austin in 2011 and 2015 respectively. He received the B.Tech degree in Electronics and Communication Engineering from IIT Guwahati, India, in 2009 for which he was awarded the Institute Silver Medal as the best outgoing student in the department. His research focuses on learning, control, optimization and cyber-security in power grids and dynamical systems.