A Real Data-Driven Fault Diagnosing Method for Distribution Networks Based on ResBlock-CBAM-CNN

Power distribution systems frequently encounter various fault-causing events. Thus, prompt and accurate fault diagnosis is crucial for maintaining system stability and safety. This study presents an innovative residual block-convolutional block attention module-convolutional neural network (ResBlock-CBAM-CNN)-based method for fault cause diagnosis. To enhance diagnostic precision further, the proposed approach incorporates a multimodal data fusion model. This model combines raw on-site measurements, processed data, and external environmental information to extract relevant fault-related details. Empirical results show that the ResBlock-CBAM-CNN method, with data fusion, outperforms existing techniques significantly in fault identification accuracy. Additionally, t-SNE visualization of fault data validates the effectiveness of this approach. Unlike studies that rely on simulated datasets, this research uses real-world measurements, highlighting the practical applicability and value of the proposed model for fault cause diagnosis in power distribution systems.