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Graph Signal Processing for Power Distribution System Monitoring.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Graph Signal Processing for Power Distribution System Monitoring./
Author:	Anderson, Osten P.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	51 p.
Notes:	Source: Masters Abstracts International, Volume: 83-02.
Contained By:	Masters Abstracts International83-02.
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28543041
ISBN:	9798534694505

Graph Signal Processing for Power Distribution System Monitoring.
Anderson, Osten P.

Graph Signal Processing for Power Distribution System Monitoring. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 51 p.

Source: Masters Abstracts International, Volume: 83-02.

Thesis (M.S.)--University of California, Riverside, 2021.

This item must not be sold to any third party vendors.

Over recent decades, reduction in the costs of advanced metering infrastructure (AMI) has improved the economic feasibility for these devices to be deployed throughout distribution systems. As a result, data on power systems has proliferated. This data has enabled the development of new data-driven algorithms for distribution system control. However, this recorded data is imperfect. Data recorded by AMI devices is prone to corruption. This data is also associated with potential unknowns in physical connectivity of the power distribution networks. These problems can impede the ability for a grid operator to successfully perform essential control tasks in power distribution systems. To this end, this thesis presents novel algorithms in the domains of bad data detection and network topology change detection. The developed algorithms are built upon the field of graph signal processing, which has received minimal attention in the context of power systems. This thesis additionally contributes towards the application of the graph signal processing algorithms in power distribution systems.

ISBN: 9798534694505Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Advanced metering infrastructure

Graph Signal Processing for Power Distribution System Monitoring.
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100 1 $a Anderson, Osten P. $3 3688966
245 1 0 $a Graph Signal Processing for Power Distribution System Monitoring.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 51 p.
500 $a Source: Masters Abstracts International, Volume: 83-02.
500 $a Advisor: Yu, Nanpeng.
502 $a Thesis (M.S.)--University of California, Riverside, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Over recent decades, reduction in the costs of advanced metering infrastructure (AMI) has improved the economic feasibility for these devices to be deployed throughout distribution systems. As a result, data on power systems has proliferated. This data has enabled the development of new data-driven algorithms for distribution system control. However, this recorded data is imperfect. Data recorded by AMI devices is prone to corruption. This data is also associated with potential unknowns in physical connectivity of the power distribution networks. These problems can impede the ability for a grid operator to successfully perform essential control tasks in power distribution systems. To this end, this thesis presents novel algorithms in the domains of bad data detection and network topology change detection. The developed algorithms are built upon the field of graph signal processing, which has received minimal attention in the context of power systems. This thesis additionally contributes towards the application of the graph signal processing algorithms in power distribution systems.
590 $a School code: 0032.
650 4 $a Electrical engineering. $3 649834
650 4 $a Computer engineering. $3 621879
650 4 $a Information technology. $3 532993
650 4 $a Energy. $3 876794
650 4 $a Datasets. $3 3541416
650 4 $a Error correction & detection. $3 3480646
650 4 $a Buses. $3 870578
650 4 $a Signal processing. $3 533904
650 4 $a Decomposition. $3 3561186
650 4 $a Noise. $3 598816
650 4 $a Methods. $3 3560391
650 4 $a Algorithms. $3 536374
650 4 $a Clustering. $3 3559215
650 4 $a Neighborhoods. $3 993475
650 4 $a Eigenvalues. $3 631789
653 $a Advanced metering infrastructure
653 $a Power distribution
653 $a System control
653 $a Bad data detection
653 $a Network topology change detection
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710 2 $a University of California, Riverside. $b Electrical Engineering. $3 1669528
773 0 $t Masters Abstracts International $g 83-02.
790 $a 0032
791 $a M.S.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28543041