This Iowa State University Power Group Seminar is an online seminar only
Title: Data Analytics for Calibrating Distribution System Models and Integrating DER
Abstract: Distribution system analysis tools are often severely limited in their effectiveness by the accuracy of the model details and parameters of the grid. This presentation first discusses several physics-based Machine Learning algorithms to calibrate distribution system models and to dynamically adapt to changing conditions on the grid based on measurements. Second, methods will be presented for advancing DER integration without distribution system models. These data-driven approaches proposed can determine hosting capacity model-free using only historical customer information.
Bio: Matthew Reno is a Principal Member of Technical Staff in the Electric Power Systems Research Department at Sandia National Laboratories. His research focuses on distribution system modeling and analysis with Big Data and high penetrations of PV by applying cutting edge machine learning algorithms to power system problems. Matthew is also involved with the IEEE Power System Relaying Committee for developing guides and standards for protection of microgrids and systems with high penetrations of inverter-based resources. He received his Ph.D. in electrical engineering from Georgia Institute of Technology.
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Meeting ID: 940 8752 4568