Author Type

Graduate Student

Date of Award

2024

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Tang, Yufei

Abstract

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.

Extent

84 p.

Comments

Includes bibliography.|Thesis (MS)--Florida Atlantic University, 2024.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.

Identifier

FA00014528

Format

pdf

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