Corruption-Resilient Detection of Event-Induced Outliers in PMU Data: A Kernel PCA Approach

Kaustav Chatterjee, Nilanjan Ray Chaudhuri

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

Abstract

Bad data outliers and malicious corruption in Phasor Measurement Unit (PMU) data having signature similar to that of a highly nonlinear event-induced oulier can challenge reliable event detection when linear principal component analysis (PCA)-based metrics are used. This paper presents a moving window based kernel PCA approach for accurately detecting event-induced outliers in presence of such corruptions in data. It is demonstrated that with appropriate tuning of kernel parameters, the change in the square of the norm of principal component score between successive windows along the direction of maximum variance in feature space can be used as a metric for corruption-resilient detection of event-induced outliers. Analytical justification for the same is provided along with a bound on this change. The performance of the proposed metric is validated on both synthetic data and field measurements.

Original languageEnglish (US)
Title of host publication2019 IEEE Power and Energy Society General Meeting, PESGM 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728119816
DOIs
StatePublished - Aug 2019
Event2019 IEEE Power and Energy Society General Meeting, PESGM 2019 - Atlanta, United States
Duration: Aug 4 2019Aug 8 2019

Publication series

NameIEEE Power and Energy Society General Meeting
Volume2019-August
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Conference

Conference2019 IEEE Power and Energy Society General Meeting, PESGM 2019
CountryUnited States
CityAtlanta
Period8/4/198/8/19

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

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  • Cite this

    Chatterjee, K., & Chaudhuri, N. R. (2019). Corruption-Resilient Detection of Event-Induced Outliers in PMU Data: A Kernel PCA Approach. In 2019 IEEE Power and Energy Society General Meeting, PESGM 2019 [8973424] (IEEE Power and Energy Society General Meeting; Vol. 2019-August). IEEE Computer Society. https://doi.org/10.1109/PESGM40551.2019.8973424