Title :
Detection and visualization of power system disturbances using principal component analysis
Author :
Barocio, E. ; Pal, B.C. ; Fabozzi, Davide ; Thornhill, Nina F.
Author_Institution :
Dept. of Electr. & Electron. Eng., Imperial Coll. London, London, UK
Abstract :
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.
Keywords :
power system faults; principal component analysis; statistical testing; wide area networks; anomalous dynamic events; multivariate statistical projection method; power system disturbances; principal component analysis; statistical test; transient stability model; wide area monitoring data; Data models; Data visualization; Eigenvalues and eigenfunctions; Monitoring; Phasor measurement units; Power system dynamics; Principal component analysis;
Conference_Titel :
Bulk Power System Dynamics and Control - IX Optimization, Security and Control of the Emerging Power Grid (IREP), 2013 IREP Symposium
Conference_Location :
Rethymno
Electronic_ISBN :
978-1-4799-0199-9
DOI :
10.1109/IREP.2013.6629374