基于微振动宽带相位运动放大与深度学习的输电线路索张力测量方法

PDF(4614 KB)

振动与冲击 ›› 2025, Vol. 44 ›› Issue (1) : 163-176.

故障诊断分析

基于微振动宽带相位运动放大与深度学习的输电线路索张力测量方法

姜岚1,2，叶卿辰*1,2，唐波1,2，程若恒3，陶文心1,2，黄荥1,2

作者信息 +

Measurement method of cable tension in transmission lines based on micro vibration broadband phase-based motion amplification and deep learning#br#

JIANG Lan1,2, YE Qingchen*1,2, TANG Bo1,2, CHENG Ruoheng3, TAO Wenxin1,2, HUANG Xing1,2

Author information +

文章历史 +

摘要

索类构件在输电线路中广泛分布，其张力值及变化情况是影响输电线路本质安全的关键因素，因此也是输电线路工程施工及运维期间状态监测的重点。传统的索张力测量方法存在精度低、环境要求高、难以带电监测等问题，在输电线路中不具备普适性。本文提出宽带相位运动放大方法(The Broad-Band Phase-Based Motion Magnification，BPMM)与深度学习语义分割结合的图像张力测量方法，通过增强图像振动幅度，实现环境激励下输电线路索类构件微振动图像的放大。为去除BPMM算法对于振动视频处理后出现的噪音伪影问题同时提升识别精度，提出基于深度学习U-Net网络与水平集损失熵的联合分割方法来提取索类构件形心，实现了微振动像素变化量的准确拾取，进而通过频域分析得到自振频率并计算索张力。试验及工程应用表明：基于微振动放大的输电线路索类构件张力测量方法能有效识别环境激励下索微小振动变化，测得的索张力值与传感器测量值相比，误差在6%以内，实现了输电线路索类构件张力的高精度、非接触测量，解决了输电线路张力带电测量困难的问题。

Abstract

Cable components are widely distributed in transmission lines, and their tension values and changes are the key factors affecting the intrinsic safety of transmission lines, so they are also the focus of condition monitoring during the construction and operation of transmission lines. The traditional cable tension measurement method has some problems, such as low precision, high environmental requirements, difficult to monitor live line, etc., it is not universal in transmission lines. In this paper, an image tension measurement method combining The Broad-Band Phase-Based Motion Magnification (BPMM) and deep learning semantic segmentation is proposed. By enhancing the image vibration amplitude, To achieve the amplification of microvibration images of cable components of transmission lines under environmental excitation. in order to remove the noise artifacts caused by BPMM algorithm after vibration video processing and improve the recognition accuracy, a joint segmentation method based on deep learning U-Net network and level set loss entropy is proposed to extract the centrosity of cable components and achieve the accurate pick-up of microvibration pixel changes. Then the natural vibration frequency is obtained and the cable tension is calculated by frequency domain analysis. The test and engineering application show that the strain measurement method based on microvibration amplification can effectively identify the small vibration changes of the cable under environmental excitation, and the error of the measured cable tension value is less than 6% compared with that of the sensor, which realizes the high-precision and non-contact measurement of the cable tension of the transmission line, and solves the difficult problem of the transmission line tension measurement.

导出引用

姜岚1, 2, 叶卿辰1, 2, 唐波1, 2, 程若恒3, 陶文心1, 2, 黄荥1, 2. 基于微振动宽带相位运动放大与深度学习的输电线路索张力测量方法[J]. 振动与冲击, 2025, 44(1): 163-176

JIANG Lan1, 2, YE Qingchen1, 2, TANG Bo1, 2, CHENG Ruoheng3, TAO Wenxin1, 2, HUANG Xing1, 2. Measurement method of cable tension in transmission lines based on micro vibration broadband phase-based motion amplification and deep learning#br#[J]. Journal of Vibration and Shock, 2025, 44(1): 163-176

参考文献

[1] 张瑶,王傲寒,张宏．中国智能电网发展综述[J].电力系统保护与控制,2021,49(05):180-187.
ZHANG Yao, WANG Ao-Han, ZHANG Hong. Review on the development of smart grid in China [J]. Power System Protection and Control, 2021,49(05):180-187.
[2] 刘振亚.特高压直流输电技术丛书[M].中国电力出版社,2009.
LIU Zhenya. Uhvdc Transmission Technology Series [M]. China Electric Power Press,2009.
[3] 杨文刚,王璋奇,朱伯文,等.特高压单柱拉线塔塔线体系风振响应时程分析[J].中国电机工程学报,2015,35(12):3182-3191.
YANG Wengang, WANG Zhangqi, ZHU Bowen, et al. Wind Vibration Response Time History Analysis of UHV Single Column Cable System [J]. Proceedings of the CSEE,2015,35(12):3182-3191.
[4] 王璋奇,齐立忠,王剑,等.架空输电线非同期脱冰跳跃动张力实验研究[J].振动与冲击,2016,35(22):61-65.
Wang Zhangqi, Qi Lizhong, Wang Jian, et al. Experimental study on Non-synchronous deicing jump Dynamic Tension of overhead transmission Line [J]. Journal of Vibration and Shock,2016,35(22):61-65.
[5] 李正良,张智航,王涛.双柱悬索拉线塔风致倒塌易损性分析[J].中南大学学报(自然科学版),2024,55(01):255-267.
Li Zhengliang, Zhang Zhihang, Wang Tao. Analysis of wind-induced collapse vulnerability of double-column suspended cable tower [J]. Journal of Central South University (Natural Science Edition),2024,55(01):255-267.
[6] 俞登科,李正良,施菁华,等.拉线初张力对特高压双柱悬索拉线塔受力性能影响的风洞试验研究[J].振动与冲击,2015,34(13):163-168.
YU Dengke, LI Zhengliang, SHI Jinghua, et al. Wind tunnel test Study on the influence of initial tension on the mechanical performance of UHV double-column suspension cable tower [J].Journal of Vibration and Shock,2015,34(13):163-168.
[7] 汪峰,曾超,薛春林,等.大跨越架空输电导线铝部应力分布试验[J].重庆大学学报,2024,47(03):66-74.
Wang Feng, Zeng Chao, Xue Chunlin, et al. Stress Distribution Test of Aluminum Part of Long-span overhead Transmission Wire [J]. Journal of Chongqing University,2024,47(03):66-74.
[8] 李正良,王泽宇,王涛,等.覆冰多分裂输电导线舞动的动态张力随机分析与可靠度评估[J/OL].振动工程学报:1-10[2024-05-22].
Li Zhengliang, Wang Zeyu, Wang Tao, et al. Stochastic analysis of dynamic tension and reliability evaluation of ice-covered multi-split transmission wire dancing [J/OL]. Journal of Vibration Engineering :1-10[2024-05-22].
[9] 杨金显,申刘阳,郑泽南,等.基于动态模态分解的输电杆塔松动检测[J].振动与冲击,2023,42(19):204-211.
Yang Jinxian, Shen Liuyang, Zheng Zen-nan, et al. Transmission tower Loosening Detection based on Dynamic Mode Decomposition [J]. Journal of Vibration and Shock,2023,42(19):204-211.
[10] 高鹏,臧五岳,李丹煜,等.输电导线结冰数值模拟与影响因素分析[J].振动与冲击,2023,42(20):77-85.
GAO Peng, ZANG Wuyue, Li Danyu, et al. Numerical Simulation of Icing in Transmission Wires and Analysis of Influencing Factors [J].Journal of Vibration and Shock,2023,42(20):77-85.
[11] 王宇赛,张凯,肖方园,等.基于锚杆构件振动响应特征的锚杆轴力无损检测方法[J].振动与冲击,2023,42(13):119-126+146.
Wang Yusai, Zhang Kai, Xiao Fangyuan, et al. A non-destructive testing method for axial Force of anchor bolt based on Vibration response Characteristics of anchor bolt components [J].Journal of Vibration and Shock, 2019,42(13):119-126+146.
[12] 郝艳捧,梁苇,潘锐健，等.输电线路智能带电检修关键技术研究综述[J].电力自动化设备,2022,42(02):163-175.
HAO Yanbao, Liang Wei, Pan Ruijian, et al. Review of key technologies for intelligent on-line maintenance of transmission lines [J]. Electric Power Automation Equipment, 2022,42(02):163-175.
[13] 夏樟华,陈启煌,林友勤，等.基于振动理论的灌浆拉索索力计算方法[J/OL].应用力学学报，1-14[2023-09-26].
XIA Zhanghua, Chen Qihuang, Lin Youqin, et al. Calculation method of grouting cable force based on Vibration Theory [J/OL].Chinese Journal of Applied Mechanics:1-14[2023-09-26].
[14] 何雄君,杨永超,肖祥等.基于有限元等效索长的振动频率法[J].桥梁建设,2016,46(06):40-44.
He Xiongjun, Yang Yongchao, Xiao Xiang et al. Vibration frequency method based on finite element equivalent cable Length [J].Bridge Construction,2016,46(06):40-44.
[15] 周海涛,李浩军,康远勇，等.基于增强八叉树的电网线路巡检激光点云数据切片技术研究[J].高电压技术,2023,49(S1):97-102.
ZHOU Haitao, Li Haojun, Kang Yuanyong, et al. Research on laser point cloud data slicing technology for power grid line inspection based on enhanced Octree [J].High Voltage Technology,2023,49(S1):97-102.
[16] 王建,王翔,周智敏.微型桥梁索力测量毫米波雷达[J].国防科技大学学报,2022,44(02):118-122.
Wang Jian, Wang Xiang, Zhou Zhimin. Millimeter wave radar for bridge cable force measurement [J].Journal of National University of Defense Technology, 2022, 44(02):118-122.
[17] 黄侨,王翼超,任远，等.基于微波干涉雷达的斜拉桥索力测量方法[J].河海大学学报(自然科学版),2022,50(06):144-151.
HUANG Qiao, WANG Yichao, REN Yuan, et al. Measurement method of cable-stayed bridge cable force based on microwave interferometer radar [J]. Journal of Hohai University(Natural Science Edition), 2022, 50(06): 144-151.
[18] 郭隆强,何赟泽,杜旭，等.基于视觉修正的激光雷达体积测量方法[J].仪器仪表学报,2023,44(10):48-59.
GUO Longqiang, HE Yunze, DU Xu，et al. Volume measurement method of laser radar based on vision correction [J].Chinese Journal of Scientific Instrument, 2023,44(10):48-59.
[19] 晏班夫,陈泽楚,朱子纲.基于非接触摄影测量的拉索索力测试[J].湖南大学学报(自然科学版),2015,42(11):105-110.
YAN Banfu, CHEN Zechu, ZHU Zigang. Cable Force Measurement based on non-contact Photogrammetry [J].Journal of Hunan University (Natural Science Edition), 2015,42(11):105-110.
[20] 姜岚,程若恒,唐波,等.基于图像边缘识别的输电线路拉线塔拉线张力测试方法[J].高电压技术,2022,48(11):4469-4477.
JIANG Lan, CHENG Ruoheng, TANG Bo, et al. Tension test method of transmission line Tara line based on image edge recognition [J]. High Voltage Technology,2022,48(11):4469-4477.
[21] 张宇航,苏成,邓逸川.基于欧拉运动放大算法的桥梁索力测试方法研究[J].图学学报,2021,42(6):941-947.
ZHANG Yuhang, SU Cheng, DENG Yichuan. Research on Bridge Cable Strength Test Method Based on Euler Motion Amplification Algorithm [J]. Journal of Graphics, 2021,42(6):941-947.
[22] 孔烜, 罗奎, 邓露,等. 基于宽带相位运动放大与机器视觉的结构振动频率识别[J/OL].土木工程学报,2023,56(10):105-117.
KONG Xuan, LUO Kui, DENG Lu, et al. Structural vibration frequency recognition based on wide-band phase motion amplification and machine vision [J/OL].Journal of Civil Engineering,2023,56(10):105-117.
[23] 刘赫,杨代勇,赵春明，等.利用深度学习方法提取配网线路舞动特征[J].电力系统及其自动化学报,2023,35(5):89-94.
LIU He, YANG Daiyong, ZHAO Chunming, et al. Using Deep Learning Method to extract line Dancing Characteristics of Distribution Network [J].Journal of Electric Power Systems and Automation,2023,35(5):89-94.
[24] 杨传凯,孔志战,谢倩楠,等.基于DeepLab v3+深度卷积网络的输电导线图像识别方法[J].电力工程技术,2021,40(04):189-194.
YANG Chuankai, KONG Zhizhan, XIE Qiannan, et al. Transmission wire image recognition method based on DeepLab v3+ Deep Convolutional network [J].Electric Power Engineering Technology,2021,40(04):189-194.
[25] Lee, Hun Y，HYUNGCHUL K, et al. Vibration detection of stay-cable from low-quality CCTV images using deep-learning-based dehazing and semantic segmentation algorithms[J]. ENGINEERING STRUCTURES, 2023,292.
[26] Yang, Han X, JIAO H, et al. Semantic Image Segmentation Based Cable Vibration Frequency Visual Monitoring Using Modified Convolutional Neural Network with Pixel-wise Weighting Strategy[J]. REMOTE SENSING,。2021,13(8):1466-1466.
[27] Kim, Youngeun, Seunghyeon, et al. CNN-based semantic segmentation using level set loss[J]. arXiv,2019.
[28] 李阳,何登科.基于水平集方法和U-Net网络的地震初至拾取技术[J].矿业科学学报,2022,7(04):437-445.
LI Yang, HE Dengke. Seismic first arrival picking technology based on level set method and U-Net network [J].Journal of Mining Science,2022,7(04):437-445.
[29] Eitner M, Miller B, Sirohi J, et al. Effect of broad-band phase-based motion magnification on modal parameter estimation [J]. Mechanical Systems and Signal Processing, 2021, 146:106995.
[30] Ronneberger, O., Fischer, et al. U-net:Convolutional networks for biomedical image segmentation. Lecture Notes in Computer Science(Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015,9351,234–241.
[31] Long, J., Shelhamer,et al. Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2015,07-12-June,431–440.
[32] 张云龙,张家源,钱雪松,等.环境激励下桥梁模态参数识别的频谱方法综述[J].吉林大学学报(工学版), 2023,53(06):1580-1591.
ZHANG Yunlong, ZHANG jiayuan, QIAN Xuesong, et al. A review of spectral methods for identifying modal parameters of bridges under environmental excitation [J].Journal of Jilin University (Engineering Edition), 2023,53 (06): 1580-1591