Paper Info
Title
Fault localization in Smart Grid using wavelet analysis and unsupervised learning
Abstract
A wavelet based fault localization method in Smart Grid (SG) systems is proposed in this paper. In SG systems, voltage, current, frequency and phase measurements can be collected in real-time using phasor measurement units (PMUs). Based on the wavelet analysis of these measurements, the signal features can be extracted by computing the maximum wavelet transform coefficients (WTCs) and further processing them with a new hybrid clustering algorithm. The clustered signal features then form a fault contour map which can be used to locate faults in the SG system accurately. Both long-term and short-term faults of transmission line, transformer, generator, and load, which are major components of SG systems, are simulated in PSCAD and PowerWorld using the IEEE New England 39-bus system to verify the proposed method. The numerical results demonstrate the feasibility and effectiveness of our proposed method for accurate fault localization in SG systems.
Year
DOI
Venue
2012
10.1109/ACSSC.2012.6489031
ACSCC
Keywords
DocType
ISSN
phasor measurement units,ieee new england 39-bus system,frequency measurements,signal feature extraction,generator,wavelet analysis,pattern clustering,hybrid clustering algorithm,wavelet transforms,signal feature measurements,clustered signal features,wavelet-based multiresolution analysis,fault contour map,short-term faults,powerworld,phasor measurement,smart grid system,pscad,voltage measurements,power system faults,feature extraction,transformer,power engineering computing,wtc,smart power grids,transmission line,maximum wavelet transform coefficients,pmu,fault location,current measurements,wavelet based fault localization method,long-term faults,phase measurements,sg systems,unsupervised learning,smart grid monitoring
Conference
1058-6393
ISBN
Citations 
PageRank 
978-1-4673-5050-1
2
0.35
References 
Authors
0
3
Name
Order
Citations
PageRank
Huaiguang Jiang1245.11
Jun Jason Zhang212218.78
David W. Gao320.35