Online identification of bad synchrophasor measurements via spatio-temporal correlations

Meng Wu, Le Xie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations

Abstract

In order to obtain high-quality synchrophasor data prior to further power system applications such as state estimation and dynamic security assessment, this paper proposes an online data-driven algorithm to identify low-quality synchronphasor measurements caused by either physical instrumentation errors or intentional malicious attacks. The algorithm applies density-based local outlier factor (LOF) analysis and identify low-quality synchronphasor measurements which exhibit an outlier pattern of spatio-temporal correlation. The benefits of the proposed algorithm include: 1) it has fast computation performance, which is desirable for online application; 2) it is capable of identifying low-quality synchrophasor measurements during both normal and eventful operating conditions; 3) it is purely data driven, without involving any knowledge on network parameters or topology, which avoids the impact of parameter/topology errors on detection results.

Original languageEnglish (US)
Title of host publication19th Power Systems Computation Conference, PSCC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9788894105124
DOIs
StatePublished - Aug 10 2016
Externally publishedYes
Event19th Power Systems Computation Conference, PSCC 2016 - Genova, Italy
Duration: Jun 20 2016Jun 24 2016

Publication series

Name19th Power Systems Computation Conference, PSCC 2016

Other

Other19th Power Systems Computation Conference, PSCC 2016
Country/TerritoryItaly
CityGenova
Period6/20/166/24/16

Keywords

  • Bad data detection
  • data mining
  • data quality improvement
  • local outlier factor
  • synchrophasor

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Energy Engineering and Power Technology

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