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@inproceedings{Barocio:2013:10.1109/IREP.2013.6629374,
author = {Barocio, E and Pal, BC and Fabozzi, D and Thornhill, NF},
doi = {10.1109/IREP.2013.6629374},
title = {Detection and visualization of power system disturbances using principal component analysis},
url = {http://dx.doi.org/10.1109/IREP.2013.6629374},
year = {2013}
}

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TY  - CPAPER
AB  - In this paper, a multivariate statistical projection method based on Principal Component Analysis (PCA) is proposed for detecting and extracting unusual or anomalous events from wide-area monitoring data. The method combines PCA with statistical test to detect and analyze anomalous dynamic events from measured data. Simulations based on a transient stability model of the New England Test System are used to demonstrate the ability of the method to detect and extract system events from wide-area data. © 2013 IEEE.
AU  - Barocio,E
AU  - Pal,BC
AU  - Fabozzi,D
AU  - Thornhill,NF
DO  - 10.1109/IREP.2013.6629374
PY  - 2013///
TI  - Detection and visualization of power system disturbances using principal component analysis
UR  - http://dx.doi.org/10.1109/IREP.2013.6629374
ER  -

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