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SUMMARY:High-dimensional data analytics using low-dimensional models in po
 wer systems - Meng Wang (Rensselaer Polytechnic Institute)
DTSTART:20190111T113000Z
DTEND:20190111T123000Z
UID:TALK116845@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION: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 co
 unterpart. One recent research interest is to develop computationally effi
 cient data-driven methods to convert data into information.<br> <br> This 
 first part of the talk discusses our proposed missing data recovery and er
 ror correction methods for synchrophasor data. The low data quality curren
 tly prevents the implementation of synchrophasor-data-based real-time moni
 toring and control. This second half of the talk discusses our proposed pr
 ivacy-preserving data collection framework for smart meters. We developed 
 load pattern extraction methods from highly noisy and quantized smart mete
 r data such that the estimated load pattern is only accurate for the opera
 tor\, and the information is obfuscated to a cyber intruder with partial m
 easurements. The common theme of the two projects is to exploit the intrin
 sic low-dimensional structures in the data to develop fast algorithms for 
 nonconvex problems with analytical performance guarantees.
LOCATION:Seminar Room 1\, Newton Institute
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