Graph neural network and SVM-based approach for output prediction of three-phase matrix converter

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.