Review of Low-Rank Data-Driven Methods Applied to Synchrophasor Measurement

Meng Wang, Joe H. Chow, Denis Osipov, Stavros Konstantinopoulos, Shuai Zhang, Evangelos Farantatos, Mahendra Patel

Research output: Contribution to journalReview articlepeer-review

8 Scopus citations


There is a growing acceptance of using synchrophasor data collected over large power systems in control centers to enhance the reliability of power system operations. The spatial and temporal nature of power system ambient and disturbance response allows the analysis of large amount of synchrophasor data by low-rank methods. This paper provides an overview of several applications of synchrophasor data utilizing the low-rank property. The tools to capitalize on the low-rank property include matrix completion methods, tensor analysis, adaptive filtering, and machine learning. The applications include missing data recovery, bad data correction, and disturbance recognition.

Original languageEnglish (US)
Pages (from-to)532-542
Number of pages11
JournalIEEE Open Access Journal of Power and Energy
StatePublished - 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


  • Synchrophasor data
  • adaptive filtering
  • low rankness
  • matrix completion
  • tensor analysis


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