Online Detection of Low-Quality Synchrophasor Measurements: A Data-Driven Approach

Meng Wu, Le Xie

Research output: Contribution to journalArticlepeer-review

63 Scopus citations


In this paper, an online data-driven approach is proposed for the detection of low-quality synchrophasor measurements. The proposed method leverages the spatio-temporal similarities among multiple-time-instant synchrophasor measurements and formulates the low-quality synchrophasor data as spatio-temporal outliers. A density-based local outlier detection technique is proposed to detect the spatio-temporal outliers. This data-driven approach involves no system modeling information. The detection algorithm can operate under both normal and fault-on system conditions, with fast computation speed suitable for online applications. Case studies on both synthetic and real-world synchrophasor data verify the effectiveness of the proposed approach.

Original languageEnglish (US)
Article number7762220
Pages (from-to)2817-2827
Number of pages11
JournalIEEE Transactions on Power Systems
Issue number4
StatePublished - Jul 2017
Externally publishedYes


  • Data mining
  • data quality improvement
  • outlier detection
  • spatio-temporal similarity
  • synchrophasor

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


Dive into the research topics of 'Online Detection of Low-Quality Synchrophasor Measurements: A Data-Driven Approach'. Together they form a unique fingerprint.

Cite this