电器与能效管理技术Issue(3)：81-88,8.DOI:10.16628/j.cnki.2095-8188.2026.03.011
基于多维信息融合和增强深度学习的电力电缆故障识别算法设计
Design of Power Cable Fault Identification Algorithm Based on Multi-Dimensional Information Fusion and Enhanced Deep Learning
摘要
Abstract
Aiming at the problems of insufficient accuracy and low recognition efficiency of traditional power cable fault identification algorithms in complex operating environments,a fault identification algorithm based on multi-dimensional information fusion and enhanced deep learning is designed.The multi-dimensional information fusion technology is utilized to effectively integrate the electrical parameters,environmental parameters and operational status parameters of power cables,providing high-quality input data for fault identification.By introducing the residual connection mechanism into traditional deep learning algorithms,an enhanced deep learning algorithm is formed,effectively solving the vanishing gradient problem and improving the feature extraction ability and recognition accuracy.The experimental results show that the fault identification accuracy and efficiency of this algorithm both reach over 99％,significantly superior to multiple comparison methods,providing effective technical support for ensuring the safe and stable operation of the power system.关键词
电力电缆/故障识别/多维信息融合/增强深度学习/识别准确率Key words
power cable/fault identification/multi-dimensional information fusion/enhanced deep learning/recognition accuracy分类
信息技术与安全科学引用本文复制引用
王刚,铁源,何峰,曹新燕,黄贵武..基于多维信息融合和增强深度学习的电力电缆故障识别算法设计[J].电器与能效管理技术,2026,(3):81-88,8.基金项目
国网兰州供电供司项目(B3270125000A) （B3270125000A）
