IABC-Optimized 1D-CNN for Robust Open-Circuit Fault Diagnosis of IGBT Inverter Modules in Marine Ranching Power Systems

To address the challenges of high feature similarity and severe noise interference in the open-circuit fault diagnosis of IGBT inverter modules under harsh marine conditions, this paper proposes an improved artificial bee colony-optimized one-dimensional convolutional neural network (IABC-1D-CNN) for robust fault diagnosis in marine ranching power systems. This study provides a MATLAB R2024a/Simulink-based feasibility validation rather than hardware or field verification. First, a photovoltaic grid-connected inverter simulation model is established to generate three-phase current signals under different operating conditions and fault states, and a sliding-window segmentation method combined with data augmentation is employed to improve sample diversity. Then, the improved artificial bee colony algorithm, incorporating differential evolution and genetic strategies, is used to globally optimize the key hyperparameters of the 1D-CNN, thereby improving convergence efficiency and model stability. Based on the optimized architecture, the proposed model enables automatic feature extraction and accurate identification of IGBT open-circuit faults under complex marine environments. Experimental results show that the proposed method achieves high diagnostic accuracy under both noise-free and noisy conditions. Under signal-to-noise ratios (SNRs) of 20 dB, 15 dB, 10 dB, and 0 dB, the diagnostic accuracies reach 99.55%, 98.86%, 97.27%, and 89.25%, respectively, consistently outperforming Baseline 1D-CNN, CNN-LSTM, and ELM. These results demonstrate that the proposed method provides a simulation-validated diagnostic framework with strong classification accuracy and noise robustness, while practical deployment requires further HIL and field-data validation.