Abstract: Quickest Online Distributed Disturbance Allocation for Large Scale Power Grid

Timely detection of the start time and location of disturbance is critical to power grid. The information helps operators quickly catch the disturbance events over wide areas and allows time for taking remedial reactions. In this paper, we proposed a Quick Online Distributed Disturbance Allocation (QODDA) scheme to detect the geographical location of disturbances in large scale power grid. The proposed QODDA system consists of two function blocks: i) a Singular Spectrum Analysis (SSA) based algorithm to quickly detect the arrival time of a disturbance at distributed sensors, and ii) a novel Temporal Scanning algorithm that is able to accurately identify the geolocation of the disturbance source point. Leveraging the real-world Frequency Disturbance Reorders (FDR) measurement data set from FNET, the experimental results have verified that the QODDA scheme is not only quicker and more robust in the noisy environments, but also is able to capture and allocate more subtle disturbance that the existing approaches cannot detect.


Dr. Yu Chen is an Associate Professor of Electrical and Computer Engineering at the Binghamton University – State University of New York (SUNY). He received the Ph.D. in Electrical Engineering from the University of Southern California (USC) in 2006. His research interest lies in Trust, Security and Privacy in Sustainable & Survivable Computing technologies. He has authored or co-authored more than 100 research papers in refereed journals, conferences, and book chapters. His research has been funded by NSF, DoD, AFOSR, AFRL, New York State, and industrial partners. He has served as reviewer for NSF panels and for international journals, and on the Technical Program Committee (TPC) of prestigious conferences including IEEE/ACM Transaction on Networking, IEEE Transaction on Information Forensics and Security, and  IEEE INFOCOM, etc. He is a member of ACM, IEEE, and SPIE.