A Dual-Stream Network with Dynamic Graph Convolution and Attention-Based BiGRU for IGBT Open-Circuit Fault Diagnosis in T-NPC Three-Level Inverters

Abstract

Existing CNN, TCN, residual, and lightweight network methods have achieved good performance in IGBT open-circuit fault diagnosis, but they often overlook the non-Euclidean relationships among signals. To address this limitation, this paper proposes a parallel graph–temporal network for T-NPC three-level inverters. A shared CNN extracts compact features from the three-phase currents and voltages, while the Sinkhorn–Wasserstein distance constructs a sample-level weighted dynamic graph for GCN-based relationship extraction. In parallel, BiGRU with global attention captures temporal information. Unlike fixed or equally weighted graphs, the proposed method adapts signal connections to different fault conditions. Furthermore, simulation models are constructed in MATLAB/Simulink, and the T-NPC converter operation is emulated on a real-time simulator Starsim MT6060. The proposed dual-stream model classifies 21 fault states, achieving an average validation accuracy of 99.88%, while maintaining high accuracy under severe noise.