[1]周 健,李 岩,张克声.变电站二次系统设备屏柜线套标签智能识别系统[J].机械与电子,2018,(11):67-70.
　ZHOU Jian,LI Yan,ZHANG Kesheng.Intelligent Identification System for Equipment Screen Cabinet Line Label of Substation Secondary System[J].Machinery & Electronics,2018,(11):67-70.
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变电站二次系统设备屏柜线套标签智能识别系统
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《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2018年11期
页码:
67-70
栏目:
自动控制与检测
出版日期:
2018-11-24

文章信息/Info

Title:
Intelligent Identification System for Equipment Screen Cabinet Line Label of Substation Secondary System
文章编号:
1001-2257(2018)11-0067-04
作者:
周 健1; 李 岩2; 张克声3
(1.江苏省送变电有限公司,江苏 南京 210000; 2.武汉映瑞电力科技有限公司,湖北 武汉 430000; 3.贵州理工学院电气与信息工程学院,贵州 贵阳550003)
Author(s):
ZHOU Jian1; LI Yan2;  ZHANG Kesheng3
(1.Jiangsu Power Transmission & Transformation Corporation,Nanjing 210000,China; 2.Wuhan INRE Power Technology Co., Ltd.,Wuhan 430000,China; 3.School of Electrical Information Engineering, Guizhou Institute of Technology, Guiyang 550003, China)
关键词:
变电站二次系统;  屏柜端子排接线;  深度学习;  标签信息识别
Keywords:
Substation secondary system;  screen cabinet wiring;  deep learning algorithm;  power supply reliability
分类号:
TM64
文献标志码:
A
摘要:
给出了一种变电站二次系统设备屏柜线套标签智能识别系统的设计方案,阐述了构成该系统各模块之间的信号传递流程。通过基于人工智能深度学习算法的定位和识别拍摄的屏柜端子排现场接线照片,获取接线线套标签信息,将其与电子图纸中的接线标准信息进行对比,生成检查报告对运维人员进行接线错误实时警告提醒。从而减轻变电站运维人员的劳动强度,缩短操作时间,消除因接线错误所引起的变电站运行安全隐患。
Abstract:
We presented a design scheme of the intelligent identification system for the screen cabinet line label of secondary system equipment in a substation, and elaborated the signal transmission flow between each module of the system. Through the positioning and recognition based on the artificial intelligence deep learning algorithm, the information of the wiring line sets of the screen cabinets was compared with the wiring standard information in the electronic drawings, and an inspection report was generated to give a real-time error warning on the operation and maintenance staff. As a result, the labor intensity of the operation and maintenance staff of substations can be reduced, the operation time can be shortened, and the potential safety hazards of substations caused by wiring errors can be eliminated.

参考文献/References:

[1] 鲍伟,高翔,沈冰,等.智能变电站非侵入式测试技术研究[J].电力系统保护与控制,2015,43(20):125-129.
[2] 陈安伟,乐全明,张宗益,等.基于机器人的变电站开关状态图像识别方法[J].电力系统自动化,2012,36(06):101-105.
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[4] SHI B, BAI X, YAO C. An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 39(11):2298-2304.
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备注/Memo

备注/Memo:
收稿日期:2018-08-29
作者简介:周 健(1976- ),男,江苏南通人,高级工程师,研究方向为继电保护及自动化技术。
更新日期/Last Update: 2018-11-24