The Data Science Transdisciplinary Area of Excellence (TAE) organizes and sponsors different kinds of events, which are described below. Explore events in the menu on the right hand side of the page. You can find webcasts of some talks on the "Webcasting" page. Subscribe to our Google Calendar to keep in touch.
The Data Salon is our signature event. It is an informal gathering where researchers on campus share ongoing data-related work with the data science community, with the objective of communicating with those outside of their immediate field. We then open discussion to all attendees with the goal of developing and identifying strategies, methods and related questions of interest to the researcher and data scientists. The concept of a "salon" is for a researcher to present problems and research opportunities in a domain-independent fashion that invites contributions and collaborations from different disciplines, rather than an evaluation of the merits of a specific result, as would be the case for a conventional research seminar.
Invited Speaker Series
For our Invited Speaker Series, we invite leaders in data science from off campus, including researchers, administrators, executives and policy makers from universities, institutes and organizations, to share their recent research developments or their insights on the data science movement.
We organize events such as faculty-student mixers, workshops, lectures and data competitions.
We also sponsor/endorse seminars and colloquiums organized by various departments and units on campus. These events are often not initiated by the Data Science TAE, but by the individual departments or units.
Contact us if you would like to lead a discussion in a Data Salon event, nominate an invited
speaker or request our sponsorship of a seminar that your department/unit is organizing.
Integrated Dynamic State Estimation in PowerSystems
Dr. Ning Zhou
AssociateProfessor, Electrical and Computer Engineering Department
Watson School ofEngineering & Applied Science, Binghamton University
Tomake well-informed decisions, power system operators need accurate timely estimates of the operational conditions of thepower grid. Up to the present time, conventional static state estimators havebeen widely deployed in utility control centers to improve the estimationaccuracy and expand the monitoring areas. However, these estimators are no longer sufficient for monitoring themodern power grid, which is experiencing increasing uncertainty and variationdriven by the high penetration of intermittent renewable energy sources (mainlysolar and wind). In fact, conventionalstatic state estimation methods for power grids often fail to provide anyuseful information during transmission-line tripping and cascading grid failureswhen the power system rapidly changes,and state estimation results are crucially needed.
In this presentation, the conventional state estimation isreviewed. Also,a dynamic state estimation (DSE) approach is proposed that can not onlyestimate current operational conditions but also predict their future trendsand quantify their uncertainty. To minimize the financial cost of measurementdevices while achieving observability of important system states, observabilityand detectability studies are carried out to guide measurement placement and modelselection. Itis shown that many dynamic states in the power systems are marginallyobservable (virtually unobservable). If an observer model can be chosen to makethe eigenvalues of the corresponding states stable, the DSE can still convergeto the true value of the states.
Ning Zhou (S’01- M’05- SM’08) is currently with the Electrical and Computer EngineeringDepartment at Binghamton University as an associate professor. In 2005, hereceived his Ph.D. in Electrical Engineering with a minor in statistics fromthe University of Wyoming. From 2005 to 2013,Dr. Zhou worked as a power system engineer at the Pacific Northwest NationalLaboratory. His research interests include power system dynamics and statisticalsignal processing. Dr. Zhou is a senior member of the IEEE Power and Energy Society(PES). He has been an associate editor for IET Generation Transmission andDistribution since 2016. He is the lead author of the 2009 Technical Committee Prize Paper from the IEEE/PES Power System DynamicPerformance Committee. He has been the co-Chair of IEEEPES Working Group on Data Access and secretary of IEEE PES Task Force on OscillationSource Location since 2016. He is the recipient of the 2009 Outstanding Engineer of Year Award from IEEEPower and Energy Society (PES) Richland Chapter. He is the recipient of IEEEPES Outstanding Branch Counselor Award in 2017. He is the PI of the NSF CAREERaward titled “IntegratedDynamic State Estimation for Monitoring Power Systems under High Uncertaintyand Variation” in the year of 2019.