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

Kaustav Chatterjee; Nilanjan Ray Chaudhuri

doi:10.1109/PESGM40551.2019.8973424

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 proceeding › Conference contribution

3 Scopus citations

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 language	English (US)
Title of host publication	2019 IEEE Power and Energy Society General Meeting, PESGM 2019
Publisher	IEEE Computer Society
ISBN (Electronic)	9781728119816
DOIs	https://doi.org/10.1109/PESGM40551.2019.8973424
State	Published - Aug 2019
Event	2019 IEEE Power and Energy Society General Meeting, PESGM 2019 - Atlanta, United States Duration: Aug 4 2019 → Aug 8 2019

Publication series

Name	IEEE Power and Energy Society General Meeting
Volume	2019-August
ISSN (Print)	1944-9925
ISSN (Electronic)	1944-9933

Conference

Conference	2019 IEEE Power and Energy Society General Meeting, PESGM 2019
Country/Territory	United States
City	Atlanta
Period	8/4/19 → 8/8/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

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

Access to Document

10.1109/PESGM40551.2019.8973424

Cite this

@inproceedings{643ae02b59114cd7a9ec607be7d53057,

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

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.",

author = "Kaustav Chatterjee and Chaudhuri, \{Nilanjan Ray\}",

note = "Funding Information: ACKNOWLEDGEMENTS The financial support from NSF Award CNS1739206 is acknowledged. Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Power and Energy Society General Meeting, PESGM 2019 ; Conference date: 04-08-2019 Through 08-08-2019",

year = "2019",

month = aug,

doi = "10.1109/PESGM40551.2019.8973424",

language = "English (US)",

series = "IEEE Power and Energy Society General Meeting",

publisher = "IEEE Computer Society",

booktitle = "2019 IEEE Power and Energy Society General Meeting, PESGM 2019",

address = "United States",

}

Chatterjee, K & Chaudhuri, NR 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, 2019 IEEE Power and Energy Society General Meeting, PESGM 2019, Atlanta, United States, 8/4/19. https://doi.org/10.1109/PESGM40551.2019.8973424

Corruption-Resilient Detection of Event-Induced Outliers in PMU Data: A Kernel PCA Approach. / Chatterjee, Kaustav; Chaudhuri, Nilanjan Ray.
2019 IEEE Power and Energy Society General Meeting, PESGM 2019. IEEE Computer Society, 2019. 8973424 (IEEE Power and Energy Society General Meeting; Vol. 2019-August).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Corruption-Resilient Detection of Event-Induced Outliers in PMU Data

T2 - 2019 IEEE Power and Energy Society General Meeting, PESGM 2019

AU - Chatterjee, Kaustav

AU - Chaudhuri, Nilanjan Ray

PY - 2019/8

Y1 - 2019/8

N2 - 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.

AB - 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.

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U2 - 10.1109/PESGM40551.2019.8973424

DO - 10.1109/PESGM40551.2019.8973424

M3 - Conference contribution

AN - SCOPUS:85079062223

T3 - IEEE Power and Energy Society General Meeting

BT - 2019 IEEE Power and Energy Society General Meeting, PESGM 2019

PB - IEEE Computer Society

Y2 - 4 August 2019 through 8 August 2019

ER -

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

Abstract

Publication series

Conference

UN SDGs

All Science Journal Classification (ASJC) codes

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