i-manager Publications

Graph Neural Network and SVM-Based Approach for Output Prediction of Three-Phase Matrix Converter

Shiek Ruksana*, Sailesh Kiran Kurra**, Paven Kumar Karedla***, Challa Soummith****

* Department of EEE, Vasavi College of Engineering, Hyderabad, TS, India.

** Amazon, Texas, USA.

*** Toyato North America, Texas, USA.

**** University of Cincinnati, Cincinnati, USA.

Periodicity:July - December'2025
DOI : https://doi.org/10.26634/jdp.13.2.22342

Abstract

This paper presents a novel framework combining Graph Neural Networks (GNN) and Space Vector Modulation (SVM) to model and predict the output waveform and Total Harmonic Distortion (THD) of a three-phase matrix converter. The converter's topology is transformed into a graph structure, enabling spatial and temporal features to be extracted efficiently. SVM modulation is applied to control the switching sequence, and the resultant waveform is used as the target for training the GNN model. The model achieves high accuracy in predicting voltage profiles and THD, demonstrating the capability of ML-augmented converter analysis.

Keywords

Matrix Converter, Graph Neural Network, Space Vector Modulation, Total Harmonic Distortion, Machine Learning, Circuit Graphs.

How to Cite this Article?

Ruksana, S., Kurra, S. K., Karedla, P. K., and Soummith, C. (2025). Graph Neural Network and SVM-Based Approach for Output Prediction of Three-Phase Matrix Converter. i-manager’s Journal on Digital Signal Processing, 13(2), 25-33. https://doi.org/10.26634/jdp.13.2.22342

References

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	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Pdf	35	35	200	20
Online	15	15	200	15
Pdf & Online	35	35	400	25

Graph Neural Network and SVM-Based Approach for Output Prediction of Three-Phase Matrix Converter

Abstract

Keywords

How to Cite this Article?

References

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