GNN-Based Real-Time Fault Localization in Three-Phase Matrix Converters using Waveform Signatures

Shiek Ruksana*
* Department of Electrical and Electronics Engineering, Vasavi College of Engineering, Hyderabad, Telangana, India.
Periodicity:October - December'2025
DOI : https://doi.org/10.26634/jee.19.2.22343

Abstract

Three-phase matrix converters (MCs) are emerging as compact and efficient AC-AC power conversion systems for applications requiring bidirectional power flow and high-frequency operation. However, their susceptibility to switching faults, such as open circuits, short circuits, or gate failures, necessitates advanced fault detection mechanisms to ensure system reliability. This paper proposes a Graph Neural Network (GNN)-based approach for real-time fault localization in matrix converters using waveform signatures. The method encodes both temporal and topological characteristics of the power system, enabling precise identification and classification of fault types. Experimental validations using simulated datasets generated from MATLAB/Simulink reveal high fault localization accuracy, demonstrating the potential of GNNs in predictive maintenance for power electronics.

Keywords

Matrix Converters, Fault Localization, Power Electronics, Waveform Signatures, Deep Learning, Modulation Techniques.

How to Cite this Article?

Ruksana, S. (2025). GNN-Based Real-Time Fault Localization in Three-Phase Matrix Converters using Waveform Signatures. i-manager’s Journal on Electrical Engineering, 19(2), 50-54. https://doi.org/10.26634/jee.19.2.22343

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