Host Faculty: Professor Dariusz Czarkowski
Future smart grids can potentially generate massive amounts of new detailed data from widely deployed measurement devices at all domains (bulk generation, transmission, distribution, end-user, etc). One central issue in managing the complex, diverse and distributed data under increasingly dynamic and uncertain conditions of smart grid is the effective extraction of relevant information that can enhance situational awareness and decision making. In this presentation, two specific topics will be discussed, i.e., spatio-temporal analysis for wind farm generation forecast and synchrophasor data mining for online power system dynamic security assessment (DSA). Specifically, the first part will begin with a brief introduction to the spatial and temporal dynamics of wind farm generation observed from extensive measurement data. Then, a general distributional forecast model that can be used in stochastic unit commitment and dispatch problems will be presented. In the second part of the presentation, a data mining-based DSA approach, which is robust to the uncertainty and dynamics of both the cyber and the physical systems of smart grid, will be presented. Results from several case studies using realistic power system models and wind farm generation data, together with the insight in applying both data-driven and model-based tools for data analytics of smart grid, will be discussed.
Miao He received his B.E. degree from Nanjing University of Posts and Telecommunications, China, in 2005 and his M.E. degree from Tsinghua University, China, in 2008, both in Electrical Engineering. Currently, he is a Ph.D. candidate in the School of Electrical, Computer and Energy Engineering at Arizona State University. His research interests include data analytics of smart grid, renewable generation forecast and integration, wide-area monitoring and protection of power systems. He is a student member of IEEE, IEEE Power and Energy Society (PES) and IEEE Communications Society (ComSoc).