摘要
电力电缆沿线温度的实时在线监测能够有效避免电缆过热导致的安全事故发生,分布式光纤温度传感技术由于具有耐高温、灵敏度高和抗电磁干扰等优点在电缆温度监测中得到了广泛应用。然而对长距离的电力电缆进行分布式测温时,温度信号的信噪比随距离的增长而降低,影响电缆温度测量的准确度。针对此问题,本文设计了一种基于卷积神经网络的降噪方法,在大量先验数据的基础上对神经网络的参数进行优化更新,将其应用于长距离分布式测温信号进行噪声的滤除。实验结果表明,本文的消噪方法能够将长度为11 km的分布式测温信号的噪声水平从原始的±17.5℃抑制到±1℃内,有效抑制了噪声,提高了测温准确度。
Real-time online monitoring of temperature along power cables can effectively avoid the occurrence or expansion of cable fires.Distributed fiber optic temperature sensing technology has been widely used in cable temperature monitoring by virtue of its advantages of anti-electromagnetic interference and high temperature resistance.However,the signal-to-noise ratio of temperature signals decreases with the growth of distance when distributed temperature measurement is performed on long-distance power cables,which affects the accuracy of cable temperature measurement.To address this problem,a noise reduction method based on convolutional neural network is designed in this paper,and a large number of a priori data sets are used to train the neural network noise elimination model,which is applied to the long-distance distributed temperature measurement signal for noise filtering.The experimental results show that the noise reduction method in this paper can suppress the noise level of the distributed temperature measurement signal with a length of 11 km from the original ±17.5℃ to within ±1℃,effectively suppressing the noise and improving the accuracy of temperature measurement.
作者
林静怀
尚雯珂
陈珂
黄永冰
丁晖
LIN Jinghuai;SHANG Wenke;CHEN Ke;HUANG Yongbing;DING Hui(Fujian Hoshing Hi-Tech Industrial Co.,Ltd.,Fuzhou 350001,China;School of Electrical Engineering,Xi’an Jiaotong University,Xi’an 710049,China)
出处
《电工电能新技术》
CSCD
北大核心
2024年第5期104-112,共9页
Advanced Technology of Electrical Engineering and Energy
关键词
电力电缆
分布式测温
卷积神经网络
去噪方法
power cables
distributed temperature measurement
convolutional neural networks
denoising method
