Bad data detection in PMU measurements using principal component analysis

Kaveri Mahapatra, Nilanjan Ray Chaudhuri, Rajesh Kavasseri

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

33 Scopus citations

Abstract

This paper presents an approach for bad data detection in PMU measurements during disturbances. Bad data in the form of outliers can have very similar appearance as that of system response following disturbances like faults. A principal component analysis (PCA) based approach is proposed to distinguish between the outlier caused by bad data from those caused by disturbances. The principal components (PCs) in the lower dimensional subspace capture the dynamical properties of the system and the PCs in the higher dimensional subspace represent noisy information. Using this property, it is hypothesized that outliers due to bad data will result in larger activity in the high dimensional subspace. Monte Carlo simulation results demonstrate the effectiveness of the proposed hypothesis in an example power system.

Original languageEnglish (US)
Title of host publicationNAPS 2016 - 48th North American Power Symposium, Proceedings
EditorsDavid Wenzhong Gao, Jun Zhang, Amin Khodaei, Eduard Muljadi
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509032709
DOIs
StatePublished - Nov 17 2016
Event48th North American Power Symposium, NAPS 2016 - Denver, United States
Duration: Sep 18 2016Sep 20 2016

Publication series

NameNAPS 2016 - 48th North American Power Symposium, Proceedings

Other

Other48th North American Power Symposium, NAPS 2016
Country/TerritoryUnited States
CityDenver
Period9/18/169/20/16

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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