Paper Info
Title
Spatial-temporal characterization of synchrophasor measurement systems — A big data approach for smart grid system situational awareness
Abstract
An approach for fully characterizing a synchrophasor measurement system is proposed in this paper, which aims to provide substantial data volume reduction while keep comprehensive information from synchrophasor measurements in time and spatial domains. Specifically, the optimal synchrophasor sensor placement (OSSP) problem with the effect of zero-injection buses (ZIB) is modeled and solved to ensure the minimum number of installed sensors and also the full observability of the power system. After the sensors are optimally placed, the matching pursuit decomposition algorithm is used to extract the time-frequency features for full description of the time-domain synchrophasor measurements. To demonstrate the effectiveness of the proposed characterization approach, power system situational awareness is investigated on Hidden Markov Model (HMM) based fault detection and identification using the spatial-temporal characteristics generated from the proposed approach. Several IEEE standard systems such as the IEEE 14 bus system, IEEE 30 bus system and IEEE 39 bus system are employed to validate and evaluate the proposed approach.
Year
DOI
Venue
2014
10.1109/ACSSC.2014.7094549
ACSSC
Keywords
Field
DocType
phasor measurement unit,data reduction,optimal synchrophasor sensor placement,smart grid system situational awareness,ossp problem,smart grid,zib,zero-injection buses effect,fault disturbance recorder,hmm based fault detection,optimal sensor placement,matching pursuit decomposition algorithm,time-frequency feature extraction,data volume reduction,phasor measurement,big data approach,power system situational awareness,time-domain synchrophasor measurement system,power system faults,feature extraction,fault diagnosis,ieee standard systems,power engineering computing,smart power grids,spatial-temporal characterization,big data,hidden markov models,fault identification,iterative methods,situational awareness,hidden markov model,matching pursuit decomposition,time-frequency analysis
Observability,Smart grid,System of measurement,Situation awareness,Computer science,Phasor measurement unit,Electric power system,Real-time computing,Electronic engineering,Hidden Markov model,Big data
Conference
ISSN
Citations 
PageRank 
1058-6393
1
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Huaiguang Jiang1245.11
Lei Huang210.34
Jun Jason Zhang312218.78
Yingchen Zhang49718.22
David Wenzhong Gao57511.70