Resilient Situational Awareness for Smart Grids
Abstract
The operation of modern power grids is increasingly challenged by massive renewable energy integration as well as high-impact disasters and attacks. Real-time situational awareness must be obtained from sensor data streams to support intelligent decision-making and control. Unfortunately, the cyber network and data analytics of smart grids are not immune from errors, failures, disasters, and attacks. Despite extensive recent research efforts on the resilience of the physical power infrastructure, the resilience of the cyber and algorithmic components supporting the situational awareness of the grid has not been adequately addressed.
This talk will address key concepts and methodologies enabling resilient situational awareness of smart grids against errors, failures, disasters, and attacks. Two main pillars of technology will be discussed. 1) Resilient sensor networks. This talk will present novel techniques including cross-domain resilient sensor network planning, observability-aware sensor data routing, and cyber-physical network restoration. 2) Resilient data analytics. The talk will demonstrate concepts and algorithms including adaptive state estimation under unknown measurement error statistics, cyber-physically discriminative anomaly detection, and deep-learning-based forecasting with incomplete sensor data.
Bio
Dr. Yuzhang Lin is an Assistant Professor in the Department of Electrical and Computer Engineering at New York University. He obtained his Bachelor’s and Master’s degrees from Tsinghua University, Beijing, China in 2012 and 2014, respectively, and his Ph.D. degree from Northeastern University, Boston, MA in 2018, where he received the prestigious Outstanding Graduate Student Research Award. His research interests focus on smart power grids and renewable energy systems, particularly in the aspects of data-driven modeling, situational awareness, cyber-physical resilience, and machine learning applications. He has published 5 book chapters and 45 journal papers, and his research has been widely supported by federal funding agencies including NSF, DOE, and ONR. He currently serves as the Co-Chair of the IEEE Power & Energy Society (PES) Task Force on Standard Test Cases for Power Systems State Estimation, and the Secretary of the IEEE PES Distribution System Operation and Planning Subcommittee. He is a recipient of the NSF CAREER Award.
Date and Time
Location
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- 141 Warren St
- New Jersey Institute of Technology
- Newark, New Jersey
- United States 07103
- Building: ECE
- Room Number: 202
- Click here for Map
- Starts 02 October 2023 12:00 PM UTC
- Ends 20 October 2023 02:00 PM UTC
- No Admission Charge
Speakers
Yuzhang Lin of New York University
Resilient Situational Awareness for Smart Grids
The operation of modern power grids is increasingly challenged by massive renewable energy integration as well as high-impact disasters and attacks. Real-time situational awareness must be obtained from sensor data streams to support intelligent decision-making and control. Unfortunately, the cyber network and data analytics of smart grids are not immune from errors, failures, disasters, and attacks. Despite extensive recent research efforts on the resilience of the physical power infrastructure, the resilience of the cyber and algorithmic components supporting the situational awareness of the grid has not been adequately addressed.
This talk will address key concepts and methodologies enabling resilient situational awareness of smart grids against errors, failures, disasters, and attacks. Two main pillars of technology will be discussed. 1) Resilient sensor networks. This talk will present novel techniques including cross-domain resilient sensor network planning, observability-aware sensor data routing, and cyber-physical network restoration. 2) Resilient data analytics. The talk will demonstrate concepts and algorithms including adaptive state estimation under unknown measurement error statistics, cyber-physically discriminative anomaly detection, and deep-learning-based forecasting with incomplete sensor data.
Biography:
Dr. Yuzhang Lin is an Assistant Professor in the Department of Electrical and Computer Engineering at New York University. He obtained his Bachelor’s and Master’s degrees from Tsinghua University, Beijing, China in 2012 and 2014, respectively, and his Ph.D. degree from Northeastern University, Boston, MA in 2018, where he received the prestigious Outstanding Graduate Student Research Award. His research interests focus on smart power grids and renewable energy systems, particularly in the aspects of data-driven modeling, situational awareness, cyber-physical resilience, and machine learning applications. He has published 5 book chapters and 45 journal papers, and his research has been widely supported by federal funding agencies including NSF, DOE, and ONR. He currently serves as the Co-Chair of the IEEE Power & Energy Society (PES) Task Force on Standard Test Cases for Power Systems State Estimation, and the Secretary of the IEEE PES Distribution System Operation and Planning Subcommittee. He is a recipient of the NSF CAREER Award.
Agenda
- Talk by Yuzhang Lin at 11:00 am
- Lunch box after the talk at 12:00 pm
- You don't have to be an IEEE member to attend this meeting.