Integrating PMU-data-driven and physics-based analytics for power systems operations Conference Paper uri icon


  • 2014 IEEE. This paper reports our recent work on dimensionality reduction of synchrophasor data and subsequent engineering analysis of the results. Principal component analysis (PCA) based dimensionality reduction is first applied to explore the underlying dimensionality of power systems from the data of massively deployed PMUs. Then the physical interpretations are provided with the power engineering insights: spatial interpretation suggests the coherency of generator groups; temporal analysis indicates the time-scale hierarchy of power system operations. Numerical examples using both synthetic and realistic PMU data are conducted to illustrate the potential value of combining PMU data-driven and physics-based analytics in real-time grid operations.

name of conference

  • 2014 48th Asilomar Conference on Signals, Systems and Computers

published proceedings

  • 2014 48th Asilomar Conference on Signals, Systems and Computers

altmetric score

  • 3

author list (cited authors)

  • Chen, Y., Xie, L. e., & Kumar, P. R.

citation count

  • 0

complete list of authors

  • Chen, Yang||Xie, Le||Kumar, PR

publication date

  • November 2014