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...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Electricity |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4826/6/2/19 |
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| Summary: | 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. |
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| ISSN: | 2673-4826 |