图像识别在特高压换流阀元件故障在线监测系统的应用

doi:10.11930/j.issn.1004-9649.201907086

中国电力 ›› 2020, Vol. 53 ›› Issue (11): 126-132.DOI: 10.11930/j.issn.1004-9649.201907086

图像识别在特高压换流阀元件故障在线监测系统的应用

蒋晶^1,2, 赵洋洋^1,2, 樊宏伟^1,2, 周振宇³, 董朝阳^1,2, 杨青波^1,2, 王晓丽^1,2

1. 许继集团有限公司，河南许昌 461000;
2. 许继电气股份有限公司，河南许昌 461000;
3. 中国电力技术装备有限公司，北京 100052

收稿日期:2019-07-09 修回日期:2020-03-22 出版日期:2020-11-05 发布日期:2020-11-05
通讯作者: 国家电网公司科技项目(新型换流阀和阀厅防火灭火技术研究，5200-201946091A-0-0-00)
作者简介:蒋晶(1985—)，女，工程师，硕士，从事直流输电、新能源监控系统方面的技术研究，E-mail：jiangjing_mm@163.com;赵洋洋(1985—)，男，工程师，硕士，从事柔性直流输电技术及大功率换流装置研究，E-mail：412032303@qq.com;董朝阳(1973—)，男，高级工程师，从事特高压直流输电技术研究，E-mail：chaoyangd@163.com
基金资助:
This work is supported by Science and Technology Project of SGCC (Research on New Fire Prevention Technology of Converter Valve and Valve Hall Technology, No.5200-201946091A-0-0-00)

Application of Image Recognition in On-Line Monitoring System of UHVDC Valve Element Faults

JIANG Jing^1,2, ZHAO Yangyang^1,2, FAN Hongwei^1,2, ZHOU Zhenyu³, DONG Chaoyang^1,2, YANG Qingbo^1,2, WANG Xiaoli^1,2

1. XJ Group Corporation, Xuchang 461000, China;
2. XJ Electric Company Limited, Xuchang 461000, China;
3. China Electric Power Equipment and Technology Co., Ltd., Beijing 100052, China

Received:2019-07-09 Revised:2020-03-22 Online:2020-11-05 Published:2020-11-05

摘要/Abstract

摘要： 针对特高压换流阀元件众多、实时监测困难的问题，提出了一种基于图像识别技术的换流阀元件状态在线监测方法。介绍了图像数据提取及预处理的方法，分析了晶闸管门极线脱落及螺母力矩线偏移的图像特征，实现了晶闸管门极线脱落、螺母位移等故障的智能检测。基于图像识别及故障特征提取方法，设计了一套换流阀元件状态在线监测样机，详细介绍了样机的软件和硬件设计方法。在特高压直流输电阀塔上搭建试验环境，对图像分析和故障特征提取的方法进行测试，试验结果表明，所提出的图像分析和故障特征提取方法可有效识别换流阀元件的异常工况，对换流阀元件运行异常提前预警。

关键词: 换流阀, 图像识别技术, 在线监测, 智能检测, 预警

Abstract: UHVDC valve elements are numerous and hard to monitor in real time. An on-line monitoring method is thus proposed for converter valve element conditions based on image recognition technology. The paper introduces the method to extract and pre-process image data, and analyzes the image characteristics of thyristor gate wire shedding and nut torque line displacement. The image recognition technology is adopted to realize intelligent detection of faults such as thyristor gate wire shedding and nut displacement. A prototype is designed for on-line monitoring of converter valve element conditions based on the image recognition and fault feature extraction method, and its software and hardware design methods are introduced in detail. The testing environment is set up on a valve tower of UHVDC to test the image analysis and fault feature extraction method. The testing results show that the proposed method can effectively identify the abnormal working conditions of the valve elements, and realize the early warning of their abnormal operations, and can prevent the further expansion of faults.

Key words: converter valve, image recognition technology, on-line monitoring, intelligent detection, early-warning

蒋晶, 赵洋洋, 樊宏伟, 周振宇, 董朝阳, 杨青波, 王晓丽. 图像识别在特高压换流阀元件故障在线监测系统的应用[J]. 中国电力, 2020, 53(11): 126-132.

JIANG Jing, ZHAO Yangyang, FAN Hongwei, ZHOU Zhenyu, DONG Chaoyang, YANG Qingbo, WANG Xiaoli. Application of Image Recognition in On-Line Monitoring System of UHVDC Valve Element Faults[J]. Electric Power, 2020, 53(11): 126-132.

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https://www.electricpower.com.cn/CN/Y2020/V53/I11/126

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图像识别在特高压换流阀元件故障在线监测系统的应用

Application of Image Recognition in On-Line Monitoring System of UHVDC Valve Element Faults

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图像识别在特高压换流阀元件故障在线监测系统的应用

Application of Image Recognition in On-Line Monitoring System of UHVDC Valve Element Faults

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