Adaptive separation method of vibration and motor current signal of vacuum on-load tap-changer for converter transformer

被引：0

作者：

He L. ^{[1
]}

Ding D. ^{[1
]}

Li X. ^{[2
]}

Yuan M. ^{[1
]}

Zhang Z. ^{[1
]}

Liu W. ^{[3
]}

机构：

[1] Sichuan Energy Internet Research Institute Tsinghua University, Chengdu

[2] Shanghai Humming Electric Equipment Manufacturing Co., Ltd., Shanghai

[3] Department of Electrical Engineering, Tsinghua University, Beijing

来源：

Dianji yu Kongzhi Xuebao/Electric Machines and Control | 2023年 / 27卷 / 08期

关键词：

adaptive separation; converter transformer; endpoint detection; motor current signal; on-load tap changer; vibration signal;

D O I：

10.15938/j.emc.2023.08.008

中图分类号：

学科分类号：

摘要：

On-load tap-changer (OLTC) is the core component of ultra high voltage (UHV) converter transformer, and the online monitoring technology based on driving motor current and vibration signal is a research hotspot for OLTC mechanical state in recent years. In order to automatically extract the vibration signal and drive motor current signal fragment from the online monitoring data of converter transformer OLTC, an adaptive separation method based on endpoint detection is studied in this paper. Firstly, the basic principle of endpoint detection method was introduced, and the separation process of vibration signal and motor current signal was proposed. Then, an OLTC online monitoring system was built and deployed in UHV converter station. Finally, the time-domain and frequency-domain characteristics of online monitoring data was analyzed, and the vibration signal and motor current signal were extracted by differential endpoint detection technology. Three working stages of the driving motor were divided according to the start and end points of the signals. The results show that the endpoint detection method adopted in this paper can effectively separate OLTC vibration and current motor signals, and the positioning error is less than 5 ms, which can provide a basis for OLTC online monitoring data analysis and fault diagnosis. © 2023 Editorial Department of Electric Machines and Control. All rights reserved.

引用

页码：73 / 79

页数：6

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