i-manager Publications

Graph Neural Network-Based Prediction of Output Waveform in a Single-Phase Matrix Converter

Shiek Ruksana*

Department of Electrical and Electronics Engineering, Vasavi College of Engineering, Hyderabad, Telangana, India.

Periodicity:July - September'2025

Abstract

This paper presents a Graph Neural Network (GNN) model designed to predict the output voltage waveform of a single- phase matrix converter (SPMC) functioning as a rectifier under the Sinusoidal Pulse Width Modulation (SPWM) technique. By representing the circuit topology as a graph-with electrical components as nodes and their connections as edges-the model captures the underlying physical interactions within the converter. This graph-based framework enables the network to accurately learn and replicate the behavior of the system, delivering precise waveform predictions. The proposed approach demonstrates both high accuracy and computational efficiency, making it well-suited for converter design validation and performance forecasting.

Keywords

Single-Phase Matrix Converter, Graph Neural Network, SPWM, Output Waveform Prediction, Circuit Modeling.

How to Cite this Article?

Ruksana, S. (2025). Graph Neural Network-Based Prediction of Output Waveform in a Single-Phase Matrix Converter. i-manager’s Journal on Power Systems Engineering, 13(2), 15-23.

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Graph Neural Network-Based Prediction of Output Waveform in a Single-Phase Matrix Converter

Abstract

Keywords

How to Cite this Article?

References

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