High-dimensional Data Analytics Using Low-dimensional Models – May 16, 2019
Phasor Measurement Units and smart meters provide fine-grained measurements to enhance the system visibility to the operators and reduce blackouts. The spatial-temporal blocks of collected measurements have intrinsic low-dimensional structures due to the correlations governed by the underlying physical system. The central idea of the talk is to show that one can exploit the low-dimensional structure to develop fast model-free methods for information recovery with analytical guarantees.
One example is missing data recovery and error correction for synchrophasor data. The low data quality currently prevents the implementation of synchrophasor-data-based real-time monitoring and control. We developed model-free approaches to recover the PMU data even under the extreme scenarios of simultaneous and consecutive data losses and data errors across all channels for some time. By exploiting the low-dimensional structures, we formulated the data recovery problem as nonconvex optimization problems and developed fast algorithms to find the global minimum with a linear rate.
The second example is our proposed privacy-preserving data collection framework for smart meters. One can add noise and quantize the data significantly to hide the information in individual measurements. We developed computationally efficient load pattern extraction methods from highly noisy and quantized smart meter data such that the estimated load pattern is only accurate for the operator, and the information is obfuscated to a cyber-intruder with partial measurements. This enables the data sharing among different parties without sacrificing privacy.
PRESENTED BY: Dr Meng Wang
DATE and TIME: Thursday, May 16th, 2019, 12 to 1:00 PM
LOCATION: Niskayuna Reformed Church Room, 3041 Troy Schenectady Rd, Niskayuna, NY 12309
SPONSORED BY: IEEE Computational Intelligence Society, IEEE Schenectady Section
LUNCH: Lunch will be provided.
RESERVATIONS: Please contact Supriya Tawde, at tawdessupriya@ieee.org by Wednesday, May 15th, 2019 to reserve. This event is free for IEEE members. There is a $10 fee for non-members.
THE SPEAKER: Dr Meng Wang is an Assistant Professor in the Department of Electrical, Computer and Systems Engineering at Rensselaer Polytechnic Institute. She received B.S. and M.S. degrees from Tsinghua University, China, in 2005 and 2007, respectively. She received her Ph.D. degree from Cornell University, Ithaca, NY, USA, in 2012. Prior to joining RPI, she was a postdoc research scholar at Duke University. Her research areas involve machine learning and data analytics, energy systems, signal processing, and optimization. She is a recipient of the Army Research Office Young Investigator Program (YIP) Award. She also received the School of Engineering Research Excellence Award from Rensselaer. She is a guest editor of IEEE Journal of Selected Topics in Signal Processing Special Issue on Signal and Information Processing for Critical Infrastructures in 2018.