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13 - Graph Signal Processing for the Power Grid

from Part IV - Signal Processing

Published online by Cambridge University Press:  22 March 2021

By
Anna Scaglione ,
Raksha Ramakrishna  and
Mahdi Jamei
Edited by
Ali Tajer ,
Samir M. Perlaza  and
H. Vincent Poor
Ali Tajer
Affiliation:
Rensselaer Polytechnic Institute, New York
Samir M. Perlaza
Affiliation:
INRIA
H. Vincent Poor
Affiliation:
Princeton University, New Jersey
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Summary

Graph Signal Processing (GSP) is a general theory, whose goal is to bring about tools for graph signals analysis that are a direct generalization of Digital Signal Processing (DSP). The goal of this chapter is understanding the graph-spectral properties of the signals, which are typically explained through the linear generative model using graph filters. Are PMU a graph signal that obeys the linear generative model prevalent in the literature? If so, what kind of graph-filter structure and excitation justifies the properties discussed already? Can we derive new strategies to sense and process these data based on GSP? By putting the link between PMU data and GSP on the right footing, we can determine to what extent GSP tools are useful, and specify how we can use the basic equations for gaining theoretical insight that support the observations.

Keywords

graph signal processingsamplingcompressiondimensionality reductionfault localizationcommunity detection
Type
Chapter
Information
Advanced Data Analytics for Power Systems , pp. 312 - 339
Publisher: Cambridge University Press
Print publication year: 2021

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