贾伯岩, 夏彦卫, 刘宏亮, 徐亚兵, 伊晓宇. 基于光纤传感技术的多股碳纤维复合芯导线隐蔽性缺陷检测[J]. 应用光学, 2024, 45(2): 467-474. DOI: 10.5768/JAO202445.0208004
引用本文: 贾伯岩, 夏彦卫, 刘宏亮, 徐亚兵, 伊晓宇. 基于光纤传感技术的多股碳纤维复合芯导线隐蔽性缺陷检测[J]. 应用光学, 2024, 45(2): 467-474. DOI: 10.5768/JAO202445.0208004
JIA Boyan, XIA Yanwei, LIU Hongliang, XU Yabing, YI Xiaoyu. Detection of hidden defects of multi-strand carbon fiber composite core conductor based on optical fiber sensing technology[J]. Journal of Applied Optics, 2024, 45(2): 467-474. DOI: 10.5768/JAO202445.0208004
Citation: JIA Boyan, XIA Yanwei, LIU Hongliang, XU Yabing, YI Xiaoyu. Detection of hidden defects of multi-strand carbon fiber composite core conductor based on optical fiber sensing technology[J]. Journal of Applied Optics, 2024, 45(2): 467-474. DOI: 10.5768/JAO202445.0208004

基于光纤传感技术的多股碳纤维复合芯导线隐蔽性缺陷检测

Detection of hidden defects of multi-strand carbon fiber composite core conductor based on optical fiber sensing technology

  • 摘要: 为解决大容量、大跨越输电用碳纤维导线由于隐蔽性缺陷无法检出而导致频繁断线的问题,提出了一种基于光纤传感技术的多股碳纤维复合芯导线隐蔽性缺陷检测方法。该方法通过搭建导线运行环境并模拟导线运行工况,采用基于分布式光纤布里渊散射的时域反射技术,检测碳纤维导线的温度和应变分布情况,并结合光时域反射技术,检测碳纤维导线中光纤的损耗情况。经过综合对比分析,获取可表征多股碳纤维缺陷隐蔽性缺陷的光纤温度、应变、损耗等信号特征量,并构建神经网络模型,将各信号特征量作为模型输入,通过模型训练确定模型内各权系数,使其能够有效地检测多股碳纤维复合芯导线隐蔽性缺陷。实验结果表明,该方法可有效获取各类光纤信号特征量,并且能够准确地检测各类导线隐蔽性缺陷,具有重要的实际应用价值。

     

    Abstract: In order to solve the problem of frequent disconnection of carbon fiber conductors for large-capacity and long-distance transmission due to undetectable hidden defects, a detection method for hidden defects in multi-strand carbon fiber composite core conductors based on optical fiber sensing technology was proposed. The operating environment of the wire was established and the operating conditions were simulated. The time-domain reflection technology based on distributed fiber Brillouin scattering was adopted to detect the temperature and strain distribution of carbon fiber wires, and the optical time-domain reflection technology was combined to detect the loss of optical fibers in carbon fiber wires. Comprehensive comparative analysis was conducted to obtain signal feature quantities such as fiber temperature, strain and loss that could characterize hidden defects in multi-strand carbon fibers, and a neural network model was constructed with each signal feature quantity as input to the model. Through model training, the various weight coefficients within the model were determined, enabling it to effectively detect hidden defects in multi-strand carbon fiber composite core wires. The experimental results show that this method can effectively obtain signal feature quantities of various types of fiber optic and can accurately detect various hidden defects in wires, which has important practical application values.

     

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