Graph neural network and SVM-based approach for output prediction of three-phase matrix converter
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.
Fields of Study & Subjects
Multilevel Inverters and Converters
Advanced DC-DC Converters
Sensorless Control of Electric Motors
Publication details
- Journal / source
- i-manager s Journal on Digital Signal Processing
- Volume
- 13
- Issue
- 2
- Pages
- 25–25
- Publication date
- 19 January 2026
- Language
- EN