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

fYear

2013

fDate

25-30 Aug. 2013

Firstpage

1

Lastpage

10

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/IREP.2013.6629374

Filename

6629374