测试技术学报2025，Vol.39Issue(3)：298-304,7.DOI:10.62756/csjs.1671-7449.2025037
基于改进BP神经网络的ADSS光缆弧垂监测算法
ADSS Cable Sag Monitoring Algorithm Based on Improved BP Neural Network
摘要
Abstract
The sag of power communication optical cables is an important indicator to ensure the stable operation of information communication and the power grid.A sag monitoring mechanism model based on the fusion of measurement data and static data is proposed to address the low monitoring efficiency,inabil-ity to monitor in real-time,and high operation and maintenance costs of all dielectric self-supporting(ADSS)fiber optic cable lines in complex environments.Additionally,due to the presence of nonlinear factors such as wind speed,temperature,and humidity in actual engineering sites,a sag calculation algo-rithm is proposed that integrates ideal curve based physical mechanism modeling and particle swarm optimi-zation algorithm improved BP neural network model using LiDAR calibration to achieve accurate monitor-ing of ADSS fiber optic cable line sag.The experimental results show that the proposed algorithm signifi-cantly improves the accuracy of sag monitoring compared to traditional sag monitoring methods,proving the effectiveness of the algorithm and meeting the requirements of engineering measurement accuracy.关键词
光缆弧垂/物理机理模型/误差估计/神经网络Key words
optical cable sagging/physical mechanism model/error estimation/neural networks分类
计算机与自动化引用本文复制引用
孔小红,李伟,张明,陈鹏,魏鹏超,管翰林..基于改进BP神经网络的ADSS光缆弧垂监测算法[J].测试技术学报,2025,39(3):298-304,7.基金项目
国网江苏省电力有限公司科技项目(J2023074) （J2023074）
