基于多源信息融合与一维卷积神经网络的低压供电电缆故障诊断研究OA

[Objective]To address issues in traditional low-voltage power cable fault diagnosis,such as reliance on a single signal,insufficient feature extraction,and weak anti-interference capability,an intelligent diagnostic strategy is proposed that can achieve high robustness and high-precision identification under complex operating conditions.[Methods]An intelligent diagnostic method integrating variational mode decomposition-Hilbert transform(VMD-HT)and multi-source one-dimensional convolutional neural network(MS-1DCNN)was proposed.A time-frequency analysis framework was constructed using VMD and HT to adaptively decompose signals of different modes and quantify feature parameters.Meanwhile,the MS-1DCNN structure was designed to achieve unified modeling and diagnosis of multiple types of cable faults.[Results]The experimental results demonstrated that the proposed MS-1DCNN diagnostic model outperformed conventional methods in terms of fault feature separability,classification accuracy,and stability under complex noise conditions.Superior robustness to hyperparameter variations was also verified.[Conclusion]The proposed MS-1DCNN model significantly enhances the reliability of fault identification in low-voltage cables,making it suitable for online monitoring and early warning scenarios in actual power grids.It provides a scalable technical solution for ensuring the operational safety of low-voltage distribution systems.