Neural Network Based Digital Twin of Three-Phase Inverter

Digital Twin (DT) technology has emerged as a transformative solution for real-time monitoring, fault diagnosis, and predictive maintenance in power electronic converters. Recent studies have shown that, as a black-box modeling approach, neural network (NN) based models of power electronics converter can offer some advantages over physical models. Leveraged by this, this paper explores feasibility of NN based DT for a three-phase inverter. A physics-informed NN is proposed to represent the conventional power switch-based physical model of the three-phase inverter. Experimental results demonstrate that the NN-based DT achieves high accuracy in predicting the dynamic behavior of the physical inverter while significantly reducing computational time. This indicates that the NN-based DT can be deployed on lower-end microcomputers, enhancing reactivity and performance in real-time applications.