【目的】为解决传统低压供电电缆故障诊断中存在的信号依赖单一、特征提取不足及抗干扰能力弱的问题,提出一种能够在复杂工况下实现高鲁棒性与高精度识别的智能诊断策略。【方法】本文提出一种融合变分模态分解-希尔伯特变换(VMD-HT)和多源一维卷积神经网络(MS-1DCNN)的智能诊断方法。利用VMD与HT构建时频分析框架,对不同模态信号进行自适应分解与特征参数量化;同时设计MS-1DCNN结构,实现对多类型电缆故障的统一建模与诊断。【结果】试验结果表明,所提诊断模型MS-1DCNN在故障特征分离度、分类精度以及复杂噪声环境下的稳定性方面均优于传统方法,且对超参数变化鲁棒性强。【结论】本文所提MS-1DCNN模型能够显著增强低压电缆故障的识别可靠性,适用于实际电网的在线监测和早期预警场景,为低压配电系统的运行安全提供了可推广的技术路径。

[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. Key words: low-voltage power cable; fault diagnosis; variational mode decomposition; Hilbert transform; multi-source one-dimensional convolutional neural network

国网江苏省电力有限公司科技项目(J2025032)