[Objective] Open-circuit faults of insulated-gate bipolar transistor (IGBT) in four-quadrant pulse rectifiers exhibit strong concealment characteristics

which can inevitably induce grid-side current distortion

power factor degradation

and DC-side voltage fluctuation. Traditional convolutional neural network (CNN) model applied to fault diagnosis is restricted by low diagnosis accuracy and easy trapping in local optimal solutions. To address these limitations

this paper adopts the particle swarm optimization (PSO) algorithm and proposes an intelligent fault diagnosis method integrating wavelet transform and the PSO-CNN. [Methods] Firstly

a single-phase two-level four-quadrant pulse rectifier model was established based on Simulink. Seven graded voltage fluctuation operating conditions and ten different IGBT open-circuit fault states were set to simulate complex and realistic equipment operation scenarios. Subsequently

grid-side current and DC-side voltage signals were collected. A six-layer wavelet decomposition was performed on the signals using the db3 wavelet basis

and normalized feature vectors were constructed by combining the frequency band energy coefficients. Considering the feature differences between current and voltage signals

a dual-channel CNN was built to extract fault features of grid-side current and DC-side voltage respectively. On this basis

the PSO algorithm was introduced to adaptively optimize the key hyperparameters of the CNN. [Results] Weighted F1-score and confusion matrix were taken as quantitative evaluation metrics for model performance

and comparative tests were conducted between the proposed PSO-CNN model and the traditional CNN model. The experimental results showed that compared with the traditional CNN model

the PSO-CNN model increased the weighted F1-score from 0.45 to 0.92. It effectively reduced the misclassification of faults with overlapping features and maintained stable fault diagnosis performance under voltage fluctuation conditions

with only minor recognition deviations in a small number of highly similar fault categories. [Conclusion] The combination of wavelet transform and PSO algorithm significantly enhance the feature extraction ability and classification reliability of the CNN model. The presented intelligent diagnosis method is suitable for real-time condition monitoring of four-quadrant pulse rectifiers

and it provide a feasible and reliable technical reference for intelligent operation and maintenance as well as performance improvement of various power electronic conversion devices.