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EXPLAINABLE GRAPH LEARNING FOR POWER GRID FAULT DETECTION

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Date Issued:
2024
Abstract/Description:
Short-circuit faults can cause significant damage to power grid infrastructure, resulting in costly maintenance for utility providers. Rapid identification of fault locations can help mitigate these damages and associated expenses. Recent studies have demonstrated that graph neural network (GNN) models, using phasor data from various points in a power grid, can accurately locate fault events by accounting for the grid’s topology—a feature not typically leveraged by other machine learning methods. However, despite their high performance, GNN models are often viewed as ”black-box” systems, making their decision logic difficult to interpret. This thesis demonstrates that explanation methods can be applied to GNN models to enhance their transparency by clarifying the reasoning behind fault location predictions. By systematically benchmarking several explanation techniques for a GNN model trained for fault location detection, we assess and recommend the most effective methods for elucidating fault detection predictions in power grid systems.
Title: EXPLAINABLE GRAPH LEARNING FOR POWER GRID FAULT DETECTION.
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Name(s): Bosso, Richard George , author
Tang, Yufei , Thesis advisor
Florida Atlantic University, Degree grantor
Department of Computer and Electrical Engineering and Computer Science
College of Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2024
Date Issued: 2024
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 84 p.
Language(s): English
Abstract/Description: Short-circuit faults can cause significant damage to power grid infrastructure, resulting in costly maintenance for utility providers. Rapid identification of fault locations can help mitigate these damages and associated expenses. Recent studies have demonstrated that graph neural network (GNN) models, using phasor data from various points in a power grid, can accurately locate fault events by accounting for the grid’s topology—a feature not typically leveraged by other machine learning methods. However, despite their high performance, GNN models are often viewed as ”black-box” systems, making their decision logic difficult to interpret. This thesis demonstrates that explanation methods can be applied to GNN models to enhance their transparency by clarifying the reasoning behind fault location predictions. By systematically benchmarking several explanation techniques for a GNN model trained for fault location detection, we assess and recommend the most effective methods for elucidating fault detection predictions in power grid systems.
Identifier: FA00014528 (IID)
Degree granted: Thesis (MS)--Florida Atlantic University, 2024.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Fault location (Engineering)
Neural networks (Computer science)
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00014528
Use and Reproduction: Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU