基于双分支卷积神经网络结构和多注意力机制的输电线路状态识别方法  

Transmission Line State Recognition Method Based on Dual-Branch Convolution Neural Network Structure and Multi-Attention Mechanism

作　　者：尚秋峰[1,2,3] 樊小凯 谷元宇 王健健 姚国珍[1,2,3] Shang Qiufeng;Fan Xiaokai;Gu Yuanyu;Wang Jianjian;Yao Guozhen(Department of Electronic and Communication Engineering,North China Electric Power University,Baoding 071003,Hebei,China;Hebei Key Laboratory of Power Internet of Things Technology,North China Electric Power University,Baoding 071003,Hebei,China;Baoding Key Laboratory of Optical Fiber Sensing and Optical Communication Technology,North China Electric Power University,Baoding 071003,Hebei,China)

机构地区：[1]华北电力大学电子与通信工程系,河北保定071003 [2]华北电力大学河北省电力物联网技术重点实验室,河北保定071003 [3]华北电力大学保定市光纤传感与光通信技术重点实验室,河北保定071003

出　　处：《光学学报》2024年第22期39-50,共12页Acta Optica Sinica

基　　金：国家自然科学基金(62205105);河北省自然科学基金(E2019502179);河北省省级科技计划项目(SZX2020034)。

摘　　要：针对输电线路运行状态实时监测问题,提出了一种以双分支卷积神经网络(CNN)结构为框架,融合多注意力机制的深度学习模型。时序分支利用一维卷积神经网络(1DCNN)提取振动信号的时域特征;图像分支使用连续小波变换(CWT)将振动信号转换为二维时频图像,利用二维卷积神经网络(2DCNN)提取图像的时频特征。加入通道和分支注意力机制增强模型对关键特征信息进行挖掘,避免特征冗余。使用基于相位敏感的光时域反射(Φ-OTDR)系统采集了输电线路在不同运行状态下的振动数据。实验结果表明,所提方法的识别准确率达到了94.92%,与单分支网络、1DCNNLSTM等5种深度学习模型以及传统机器学习方法相比,所提方法有着更加优越的性能。Objective Real-time monitoring and evaluation of transmission lines are essential to ensure the safety and stability of the power grid.Phase-sensitive optical time domain reflectometry(Φ-OTDR)offers advantages such as long detection range,strong resistance to electromagnetic interference,and low cost.It can directly utilize redundant fibers in optical fiber composite overhead ground wire(OPGW)and can effectively determine the operational status of transmission lines.This technology has broad application prospects in the field of transmission line status monitoring.Currently,the commonly used signal recognition methods in optical fiber vibration sensing are primarily based on machine learning and deep learning.In traditional machine learning,signal features are often extracted based on human experience before being input into a classifier,meaning that recognition performance is heavily dependent on the selection and quantity of these features.Deep learning,however,can automatically extract and select useful features from raw data,reducing the influence of manual intervention on recognition accuracy.Nonetheless,most existing deep learning methods primarily analyze a single mode of the vibration signal,limiting the model’s ability to fully extract effective feature information.To more comprehensively describe signal characteristics and improve model recognition performance,we propose a transmission line signal recognition method that integrates one-dimensional time-domain signal analysis with two-dimensional image signal analysis,using a dual-branch convolution neural network(CNN)structure and a multi-attention mechanism.Six types of on-site data from transmission lines under different operating environments are collected using theΦ-OTDR system to construct a dataset for algorithm verification.The proposed method’s recognition rate is compared with five other deep learning models,and the recognition performance of machine learning and deep learning methods on large-scale datasets is also analyzed.Methods In th

关 键 词：双分支卷积神经网络 多注意力机制 信号识别 输电线路 连续小波变换 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]