skip to content
 

High-dimensional data analytics using low-dimensional models in power systems

Presented by: 
Meng Wang Rensselaer Polytechnic Institute
Date: 
Friday 11th January 2019 - 11:30 to 12:30
Venue: 
INI Seminar Room 1
Session Title: 
Storage & Data analytics
Abstract: 
Phasor Measurement Units and smart meters provide fine-grained measurements to enhance the system visibility to the operators and reduce blackouts. The recent wealth of data is revolutionizing the conventional model-based power system monitoring and control to a modern data-driven counterpart. One recent research interest is to develop computationally efficient data-driven methods to convert data into information.<br> <br> This first part of the talk discusses our proposed missing data recovery and error correction methods for synchrophasor data. The low data quality currently prevents the implementation of synchrophasor-data-based real-time monitoring and control. This second half of the talk discusses our proposed privacy-preserving data collection framework for smart meters. We developed 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. The common theme of the two projects is to exploit the intrinsic low-dimensional structures in the data to develop fast algorithms for nonconvex problems with analytical performance guarantees.
The video for this talk should appear here if JavaScript is enabled.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons