Defect Identification and Diagnosis for Distribution Network Electrical Equipment Based on Fused Image and Voiceprint Joint Perception
As the scale of distribution networks expand, existing defect identification methods face numerous challenges, including limitations in single-modal feature identification, insufficient cross-modal information fusion, and the lack of a multi-stage feedback mechanism. To address these issues, we firs...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/13/3451 |
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| Summary: | As the scale of distribution networks expand, existing defect identification methods face numerous challenges, including limitations in single-modal feature identification, insufficient cross-modal information fusion, and the lack of a multi-stage feedback mechanism. To address these issues, we first propose a joint perception of image and voiceprint features based on bidirectional coupled attention, which enhances deep interaction across modalities and overcomes the shortcomings of traditional methods in cross-modal fusion. Secondly, a defect identification and diagnosis method of distribution network electrical equipment based on two-stage convolutional neural networks (CNN) is introduced, which makes the network pay more attention to typical and frequent defects, and enhances defect diagnosis accuracy and robustness. The proposed algorithm is compared with two baseline algorithms. Baseline 1 is a long short term memory (LSTM)-based algorithm that performs separate feature extraction and processing for image and voiceprint signals without coupling the features of the two modalities, and Baseline 2 is a traditional CNN algorithm that uses classical convolutional layers for feature learning and classification through pooling and fully connected layers. Compared with two baselines, simulation results demonstrate that the proposed method improves accuracy by 12.1% and 33.7%, recall by 12.5% and 33.1%, and diagnosis efficiency by 22.92% and 60.42%. |
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| ISSN: | 1996-1073 |