ECE Seminar Series – Dr. Zhao to discuss Statistical Signal Processing and Machine Learning as it relates to Energy Systems

January 15, 2021

ECE Research Seminar – Friday, January 22 2-3 pm

Virtual Event on Webex – For WebEx Information, scan the QR

Robust Statistical Signal Processing and Machine Learning for Cyber-Physical Energy System Reliability and Resiliency with High Penetration of DERS
with Junbo Zhao |

Abstract: The increasing penetration of stochastic and uncertain inverter-based distributed energy resources
(DERs), such as wind and solar PVs, has a considerable influence on the power system dynamics, and the
total inertia of the power grid is reduced significantly, causing reliability and resiliency concerns. On the
other hand, the power industry is transforming itself from a hierarchical, passive, and sparsely sensed
engineering system into a flat, active, and ubiquitously sensed cyber physical system. The emerging multiscale
data from phasor measurement units, SCADA, smart meters, weather, and electricity markets offers
tremendous opportunities and challenges for the industry to dynamically learn and adaptively control the
smart grid. Robust signal processing and machine learning algorithms for unlocking the potential of data and
models in smart grid are paid increasingly attention in recent years. This talk will present a set of
advancements in robust statistical signal processing and machine learning with applications in power and
energy system with DERs, including transmission system applications, such as dynamic state and parameter

estimation, cyber security, event detection and disturbance identification, transient stability prediction,
preventive control considering uncertainties, optimal power flow, as well as some distribution system
applications, i.e., topology identification, distribution system state estimation, Vol-VAR control and
optimization, microgrid energy management, etc.

Biographical info: Junbo Zhao is currently an Assistant Professor at Mississippi State University. He received the Ph.D. degree from the Department of Electrical and Computer Engineering at Virginia Tech in 2018. He was a Research Assistant Professor at Virginia Tech from May 2018 to August 2019. He did the summer internship at Pacific Northwest National Laboratory from May to August 2017. He is currently the chair of the IEEE Task Force on Power System Dynamic State and Parameter Estimation and the IEEE Task Force on Cyber-Physical Interdependency for Power System Operation and Control, the co-chair of the IEEE Working Group on Power System Static and Dynamic State Estimation, the Secretary of the IEEE PES Bulk Power System Operation Subcommittee and the IEEE Task Force on
Synchrophasor Applications in Power System Operation and Control. He has published three book chapters and more than 100 peer-reviewed journal and conference papers, with more than 50 in IEEE Transactions.
His research interests are cyber-physical power system modeling, estimation, security, dynamics and
stability, uncertainty quantification, renewable energy integration and control, robust statistical signal
processing and machine learning. He serves as the editor of IEEE Transactions on Power Systems, IEEE
Transactions on Smart Grid and IEEE Power and Engineering Letters, the Associate Editor of International
Journal of Electrical Power & Energy Systems, and the subject editor of IET Generation, Transmission &
Distribution. He was the leading guest editor of special issue of “Next Generation of Synchrophasor-based
Power System Monitoring, Operation and Control” at IET Generation, Transmission & Distribution. He is
the receipt of best paper awards of 2020 IEEE PES General Meeting and 2019 IEEE PES ISGT Asia.

* For further information contact: Dr. Jenny Du | | 5-2035

Link to video of presentation click here