In this section
@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}
}
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 -
Nina Thornhill, ABB/RAEng Professor of Process Automation
Centre for Process Systems Engineering
Department of Chemical Engineering
Imperial College London
South Kensington Campus, London SW7 2AZ
Tel: +44 (0)20 7594 6622
Email: n.thornhill@imperial.ac.uk