摘要
振动测量可以评估电力设备的运行状态，广泛应用于设备质量检验及故障识别等领域。事件感知技术能够感知设备振动引起的表面光强变化，具备测量描述物体振动行为的可行性。本文在分析事件传感器将振动行为转化为事件流数据原理的基础上，提出了将事件流数据重构为振动位移与时间关系的算法，实现振动信号幅频特征的提取；搭建振动测量试验平台，针对振动电机等电力设备开展了测量的可行性及有效性试验。试验结果表明：事件感知技术可有效感知物体表面振动行为，提出的振动测量算法普适性和稳定性好，振动频率计算准确，为工程中振动定位及幅频分析提供了一种非接触、可视化的新方法。
Power equipment vibration visualization using intelligent sensing method based on event-sensing principle
基于事件感知原理的电力设备振动智能可视化感知方法研究
Mingzhe Zhao, Xiaojun Shen, Lei Su, Zihang Dong
1.Tongji University,No.1239 Siping Road,Yangpu District,Shanghai 200092,P.R.China
2.State Grid Shanghai Municipal Electric Power Company Electric Power Science Research Institute,Shanghai 200437,P.R.China
3.Department of Electrical and Electronic Engineering,Imperial College London,London,SW7 2AZ,U.K.
收听作者1分钟语音介绍
Abstract
Keywords
Power equipment; Event sensing; Non contact measurement; Graphic display; Feasibility
Fig. 1 Event camera extracts motion signal based on brightness change
Fig. 2 Establishment of relationship between vibration displacement and time
Fig. 3 Vibration test platform
Fig. 4 Vibration motor test site
Fig. 5 Illustration of vibrating strings
Fig. 6 Frequency spectrum of string vibration
Fig. 7 Thermal diagram of main frequency of strings
Fig. 8 Event sensing of vibration motor
Fig. 9 Comparison between number of vibrating plates and vibration spectrum
Fig. 10 Vibration spectrum under different light intensities
Fig. 11 Thermal diagram of main frequency of motor vibration under different lighting intensities
Fig. 12 (a) Black and white striped pattern; (b) thermal diagram showing main frequency of motor vibration under different lighting intensities
Fig. 13 Vibration frequency thermal diagram of different light sources
Fig. 14 Vibration frequency thermal diagram of different test distance
Fig. 15 Result of stability and efficiency test
Fig. 16 Thermal diagram of primary frequency of transformer radiator
本文引文信息
Zhao M Z, Shen X J, et al. (2023) Power equipment vibration visualization using intelligent sensing method based on event-sensing principle, Global Energy Interconnection, 7(2): 228-240
赵明哲，沈小军等 (2023) 基于事件感知原理的电力设备振动智能可视化感知方法研究. 全球能源互联网（英文）, 7(2): 228-240
Biographies
Mingzhe Zhao
Mingzhe Zhao is working towards master degree at Tongji University, Shanghai. His research interests includes vibration state perception.
Xiaojun Shen
Xiaojun Shen received the Ph.D. degree in electrical engineering from the Electrical Engineering Department, Shanghai Jiaotong University, Shanghai, China, in 2007. He is currently a Professor with the Department of Electrical Engineering, Tongji University, Shanghai. His main research directions are new energy-efficient utilization and energy saving, power equipment state perception and intelligent diagnosis.
Lei Su
Lei Su received the B.S. and M.S. degrees in electrical engineering from Shanghai Jiaotong University, Shanghai, China, in 2004 and 2007, respectively. He works in State Grid Shanghai Electric Power Research Institute. His main research area is online monitoring of the status of power transmission and transformation equipment.
Zihang Dong
Zihang Dong received B.Eng degree in EEE in 2014 from the University of Liverpool,UK.He received M.Sc.Degree and Ph.D.degree in Control Systems from Imperial College London,UK,in 2015 and 2019,respectively.He was a research associate at the EEE Department of Imperial College London.He is currently an Assistant Professor at the Department of Electrical Engineering of Tongji University.His research interests include predictive control,demand response and optimisation of energy systems operation.
编辑：王彦博
审核：王 伟
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