基于主成分分析的电网设施振动模态的运动放大提取方法

doi:10.13474/j.cnki.11-2246.2025.0422

测绘通报 ›› 2025, Vol. 0 ›› Issue (4): 134-138,151.doi: 10.13474/j.cnki.11-2246.2025.0422

• 技术交流 • 上一篇

基于主成分分析的电网设施振动模态的运动放大提取方法

张可^1,2, 童旸^1,2, 黄文礼^1,2, 胡尚³, 陶庭叶³, 戴菊³

1. 安徽南瑞继远电网技术有限公司, 安徽合肥 230088;
2. 国网电力科学研究院有限公司, 江苏南京 210037;
3. 合肥工业大学土木与水利工程学院, 安徽合肥 230009

收稿日期:2025-01-20 发布日期:2025-04-28
通讯作者: 陶庭叶。E-mail:taotingye@hfut.edu.cn
作者简介:张可(1983—),男,博士,正高级工程师,研究方向为电网智能运检。E-mail:13637052098@163.com
基金资助:
国家自然科学基金(42374048);安徽省科技重大专项(202203a05020023)

Motion amplification extraction method for vibration modes of power grid facilities based on principal component analysis

ZHANG Ke^1,2, TONG Yang^1,2, HUANG Wenli^1,2, HU Shang³, TAO Tingye³, DAI Ju³

1. Anhui NARI JiYuan Power Grid Technology Co., Ltd., Hefei 230088, China;
2. State Grid Electric Power Research Institute Co., Ltd., Nanjing 210037, China;
3. College of Civil Engineering, Hefei University of Technology, Hefei 230009, China

Received:2025-01-20 Published:2025-04-28

摘要/Abstract

摘要： 在电力系统的安全运检中,电力基础设施的模态健康监测至关重要,而机器视觉方法因具有高精度、高效率、灵活可靠等优势逐渐成为结构健康监测的重要手段。针对常用的机器视觉运动放大方法中所存在的频率通带经验设置问题,本文提出了一种结合主成分分析和盲源复杂度追踪算法的振动模态提取方法,实现对微小运动信号的自主提取;为解决监测视频图像中存在的偏移、模糊等问题,提出了利用图像均值减损对比度归一系数矩阵作为图像模糊评价指标,实现对模糊图像的实时、准确剔除;与电网架空电缆桥架上所布设的加速度实测数据进行对比发现,本文方法提取的桥架振动模态与加速度计保持一致,且振动频率绝对误差在0.2 Hz以内,表明本文方法能够实现电网设施微小运动信号的实时、高精度的自主提取,从而可为电网自动化安全运检提供准确的结构模态监测数据。

关键词: 机器视觉, 运动放大, 主成分分析, 电网设施, 振动模态

Abstract: In the safe operation and inspection of power systems, modal health monitoring of power infrastructure is very important. Due to the advantages of high precision, high efficiency, flexibility and reliability, the machine vision method has become an important means of structural health monitoring. In this paper, a vibration modal extraction method based on the principal component analysis and blind source complexity tracking method, aiming to solve the problem of frequency passband empirical setting in the machine vision motion magnification method, is proposed, which can extract the micro motion signals automatically. Meanwhile, the mean subtracted contrast normalized (MSCN) coefficient is proposed as an evaluation index of image quality, which can identify and remove the blurry image in real-time and exactly. It is verified by the experiments on the overhead cable support system that the performance of the proposed method show consistency with that of the vibration accelerometer data, and the absolute error of the extracted vibration frequency is within 0.2 Hz. This indicates that the proposed method in this paper can extract the high precision micro motion signals of the power facilities in real-time, thus can provide high-quality structural modal monitoring data for the automatic operation and inspection of power systems.

Key words: machine vision, motion magnification, principal component analysis, power facility, vibration modal

中图分类号:

P237

张可, 童旸, 黄文礼, 胡尚, 陶庭叶, 戴菊. 基于主成分分析的电网设施振动模态的运动放大提取方法[J]. 测绘通报, 2025, 0(4): 134-138,151.

ZHANG Ke, TONG Yang, HUANG Wenli, HU Shang, TAO Tingye, DAI Ju. Motion amplification extraction method for vibration modes of power grid facilities based on principal component analysis[J]. Bulletin of Surveying and Mapping, 2025, 0(4): 134-138,151.

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基于主成分分析的电网设施振动模态的运动放大提取方法

Motion amplification extraction method for vibration modes of power grid facilities based on principal component analysis

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