Abstract 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. 5. Conclusions This paper proposed an IABC-optimized 1D-CNN for open-circuit fault diagnosis of IGBT inverter modules in marine ranching power systems. A MATLAB/Simulink-based photovoltaic inverter model was used to generate three-phase current signals under different fault and noise conditions. By combining sliding-window segmentation, data augmentation, and IABC-based hyperparameter optimization, the proposed method improved fault feature extraction and classification performance. The simulation results show that the proposed method achieved accuracies of 96.80%, 95.80%, 95.50%, and 88.50% under SNRs of 20 dB, 15 dB, 10 dB, and 0 dB, respectively, outperforming Attention-1D-CNN, baseline 1D-CNN, CNN-LSTM, and ELM. The confusion matrix and t-SNE visualization further confirmed its ability to extract discriminative fault features under noisy conditions. This study provides a simulation-based feasibility validation rather than hardware or field verification. Practical uncertainties, such as sensor drift, device aging, controller delay, and electromagnetic interference, have not been fully covered. In addition, the proposed method is intended for open-circuit fault diagnosis rather than microsecond-level short-circuit protection, and short-circuit and compound faults are beyond the present scope. Future work will focus on HIL experiments, field-data validation, compound-fault diagnosis, lightweight embedded deployment, and end-to-end diagnostic latency evaluation, including sampling, preprocessing, and inference.
