Circuit Dynamics Prediction via Graph Neural Network & Graph Framework Integration: Three Phase Inverter Case Study

Abstract

This article proposes an integration between a graph framework for circuit representation and a Graph neural network (GNN) model suitable for different machine learning (ML) applications. Furthermore, the paper highlights design steps for tailoring and using the GNN-based ML model for converter performance predictions based on converter circuit level and internal parameter variations. Regardless of the number of components or connections present in a converter circuit, the proposed model can be readily scaled to incorporate different converter circuit topologies and may be used to analyze such circuits regardless of the number of components used or control parameters varied. To enable the use of ML methods and applications, all physical and switching circuit properties including operating mode, components and circuit behavior must be accurately mapped to graph representation. The model scalability to other circuit types and different connections and circuits elements is also tested, while being studied in the most common DC-AC inverter in grid connected systems including filter and filterless configurations. The filtered and filterless DC-AC inverter circuits are used to evaluate the model, scoring $R^{2}$ greater than 99% in most cases and a mean square error (MSE) tending to zero.

Keywords

• Electric circuit
• bond graph
• graph neural networks (GNN)
• machine learning (ML)