DocumentCode :
648395
Title :
Dimensionality reduction and early event detection using online synchrophasor data
Author :
Yang Chen ; Le Xie ; Kumar, P. Roshan
Author_Institution :
Dept. of Electr. & Comput. Eng., Texas A&M Univ., College Station, TX, USA
fYear :
2013
fDate :
21-25 July 2013
Firstpage :
1
Lastpage :
5
Abstract :
This paper proposes a novel approach to utilizing large online synchrophasor data for early event detection in power systems. Based on principal component analysis (PCA), a linear basis of the massive online phasor measurement unit (PMU) data can be extracted to reduce the dimensionality. Using the linear basis with much reduced dimensionality, an early event detection algorithm is proposed. This algorithm is capable of predicting the changes of system operating conditions by comparing the error between PCA-projected and actual values from a few selected locations. Numerical case studies based on both PSS/E simulation and actual PMU data from Electric Reliability Council of Texas are conducted to demonstrate the efficacy of this algorithm.
Keywords :
phasor measurement; principal component analysis; PCA; PMU data; PSS/E simulation; dimensionality reduction; early event detection algorithm; online synchrophasor data; phasor measurement unit data; power systems; principal component analysis; Event detection; Monitoring; Phasor measurement units; Power systems; Prediction algorithms; Principal component analysis; Real-time systems; Dimensionality reduction; early event detection; phasor measurement unit; principal component analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Society General Meeting (PES), 2013 IEEE
Conference_Location :
Vancouver, BC
ISSN :
1944-9925
Type :
conf
DOI :
10.1109/PESMG.2013.6672974
Filename :
6672974
Link To Document :
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