Defect Identification and Diagnosis for Distribution Network Electrical Equipment Based on Fused Image and Voiceprint Joint Perception

An Chen (), Junle Liu, Silin Liu, Jinchao Fan and Bin Liao
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An Chen: China Southern Power Grid Guangdong Zhongshan Power Supply Bureau, Zhongshan 528400, China
Junle Liu: China Southern Power Grid Guangdong Zhongshan Power Supply Bureau, Zhongshan 528400, China
Silin Liu: China Southern Power Grid Guangdong Zhongshan Power Supply Bureau, Zhongshan 528400, China
Jinchao Fan: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
Bin Liao: School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China

Energies, 2025, vol. 18, issue 13, 1-18

Abstract: 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%.

Keywords: distribution network; electrical equipment; defect identification and diagnosis; image and voiceprint joint perception; CNN (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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