Document Type

Article

Publication Date

3-2025

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Source Publication

IEEE Transactions on Power Systems

Source ISSN

0885-8950

Original Item ID

DOI: 10.1109/TPWRS.2024.3447533

Abstract

Precise and timely fault diagnosis is a prerequisite for a distribution system to ensure minimum downtime and maintain reliable operation. This necessitates access to a comprehensive procedure that can provide the grid operators with insightful information in the case of a fault event. In this paper, we propose a heterogeneous multi-task learning graph neural network (MTL-GNN) capable of detecting, locating and classifying faults in addition to providing an estimate of the fault resistance and current. Using a graph neural network (GNN) allows for learning the topological representation of the distribution system as well as feature learning through a message-passing scheme. We investigate the robustness of our proposed model using the IEEE-123 test feeder system. This work also proposes a novel GNN-based explainability method to identify key nodes in the distribution system which then facilitates informed sparse measurements. Numerical tests validate the performance of the model across all tasks.

Comments

Accepted version. IEEE Transactions on Power Systems, Vol. 40, No. 2 (March 2025): 1427-1438. DOI. © 2025 Institute of Electrical and Electronic Engineers (IEEE). Used with permission.

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