Integrating PMU-data-driven and Physics-based Analytics for Power Systems Operations Conference Paper

abstract

• 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

authors

• Kumar, Panganamala
• Xie, Le

published proceedings

• CONFERENCE RECORD OF THE 2014 FORTY-EIGHTH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & 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

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Conference record / Asilomar Conference on Signals, Systems & Computers. Asilomar Conference on Signals, Systems & Computers  Journal

keywords

• Data-driven Analytics
• Dimensionality Reduction
• Phasor Measurement Unit
• Physics-based Analytics
• Principal Component Analysis

Digital Object Identifier (DOI)

• 10.1109/acssc.2014.7094633

International Standard Book Number (ISBN) 13

• 9781479982974

start page

• 1127

end page

• 1132

volume

• 2015-April

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Facssc.2014.7094633