meng wang
assistant professor,
department of electrical ,computer and systems engineering,
rensselaer polytechnic institute
data management of high-dimensional synchrophasor measurements by exploiting low-dimensional structures
rm. 3-102, west main building. dec. 23th, 2:00pm-4:00pm
phasor measurement units (pmus) can provide synchronized phasor measurements of remote points in the power system at a sampling rate of 30 samples per second or more. since the doe smart grid investment program started in 2009, the number of pmus in the north american power system has increased tremendously. the collection of high-dimensional pmu measurements will be futile if they are not supported by efficient data management and information extraction methods.
compressed sensing theory and low-rank matrix theory show that the acquisition, storage, and processing of high-dimensional data can be much simplified if the data exhibit certain low-dimensional structures such as sparsity and low-rankness. due to the wide existence of low-dimensional models, compressed sensing and low rank methods have been applied to medical imaging, computer vision, collaborative filtering, etc.
the focus of this talk is to draw a connection between pmu data management and low-rank methods. after observing the low-rankness of spatial-temporal blocks of pmu measurements in central new york power system, we propose a common framework of leveraging low-rankness for multiple pmu data management tasks. specifically, i will talk about the recovery of missing pmu data and the detection of cyber data attacks and show the theoretical and numerical results of the proposed low-rank methods.
meng wang is an assistant professor in the department of electrical, computer, and systems engineering at rensselaer polytechnic institute. she obtained her phd degree in electrical and computer engineering from cornell university in august 2012. she was a postdoc research scholar at duke university before she joined rpi in spring 2013. she obtained her bs and ms degrees in electrical engineering from tsinghua university in 2005 and 2007 respectively. her current research focus is high-dimensional data analysis and its application in power system monitoring. her boarder research interests include signal processing, optimization and networked systems.
