电气工程学报 ›› 2022, Vol. 17 ›› Issue (2): 194-200.doi: 10.11985/2022.02.022

• 电力系统 • 上一篇    下一篇

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基于物联网的有载分接开关智能监测的关键技术*

彭长青(), 尚荣艳(), 方瑞明()   

  1. 华侨大学信息科学与工程学院 厦门 361021
  • 收稿日期:2022-01-04 修回日期:2022-05-20 出版日期:2022-06-25 发布日期:2022-08-08
  • 作者简介:彭长青,男,1976年生,硕士,实验师。主要研究方向为电气设备在线监测与故障诊断。E-mail: mymail@hqu.edu.cn
    尚荣艳,女,1975年生,博士,副教授。主要研究方向为电气设备在线监测与故障诊断。E-mail: shangry@hqu.edu.cn
    方瑞明,男,1972年生,博士,教授。主要研究方向为电气设备在线监测与故障诊断。E-mail: fangrm@hqu.edu.cn
  • 基金资助:
    *厦门市高校产学研资助项目(3502Z20203036);厦门市高校产学研资助项目(3502Z20193032)

Key Technologies of Intelligent Monitoring of On-load Tap-changer Based on Internet of Things

PENG Changqing(), SHANG Rongyan(), FANG Ruiming()   

  1. College of Information Science and Engineering, Huaqiao University, Xiamen 361021
  • Received:2022-01-04 Revised:2022-05-20 Online:2022-06-25 Published:2022-08-08

摘要:

基于物联网技术框架,设计了一套嵌入式有载分接开关(On-load tap-changer,OLTC)智能监测系统。该系统主要从OLTC的振动信号中提取特征信息,对OLTC机械状态进行在线监测和故障诊断,能够及时发现故障隐患。系统采用嵌入式系统取代PC,体积小、成本低、功耗低,便于现场实施;对振动信号进行分段预处理,可以压缩原始数据,突出状态特征;设计了采取FFT-等积分带宽方法提取特征,数据处理简洁,能够有效解决嵌入式系统边缘计算算力不足的问题。试验结果表明,该系统故障诊断效率高、精确度高,具有良好的应用前景。

关键词: OLTC, 物联网, 振动, 特征提取, 嵌入式系统

Abstract:

Based on the technical framework of Internet of Things, an embedded intelligent monitoring system for on-load tap-changer(OLTC) is designed. The system mainly extracts feature information from the vibration signal of OLTC, and performs online monitoring and fault diagnosis on the mechanical operation status of OLTC, so as to discover hidden faults in time. The system adopts an embedded system to replace PC, which is small in size, low in cost, low in power consumption, and easy to implement on-site. The system preprocesses the vibration signal in segments, which can compress the original data and highlight the state characteristics. The FFT-based equal-integral-bandwidth method is designed to extract features, which makes the data processing concise and can effectively solve the problem of insufficient computing power in the edge computing of embedded systems. The experimental results show that the system has high fault diagnosis efficiency and accuracy, and has good application prospects.

Key words: OLTC, Internet of Things, vibration, feature extraction, embedded systems

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