北京航空航天大学学报 ›› 2021, Vol. 47 ›› Issue (3): 531-538.doi: 10.13700/j.bh.1001-5965.2020.0476

• 论文 • 上一篇    下一篇

电力系统厂站接线图拓扑关系检测技术

李昊1, 管荑2, 王杉1, 石玮1, 刘子鑫3, 刘晓川3   

  1. 1. 国网山东省电力公司青岛供电公司 电力调度控制中心, 青岛 266002;
    2. 国网山东省电力公司 电力调度控制中心, 济南 250001;
    3. 山东大学(青岛) 计算机科学与技术学院, 青岛 266237
  • 收稿日期:2020-08-31 发布日期:2021-04-08
  • 通讯作者: 李昊 E-mail:lihao-0717@163.com
  • 作者简介:李昊,男,硕士,工程师。主要研究方向:电力调度自动化。
  • 基金资助:
    国网山东省电力公司科技项目(5206021900TW)

Topological relation detection technology of substation wiring diagram in electric power system

LI Hao1, GUAN Ti2, WANG Shan1, SHI Wei1, LIU Zixin3, LIU Xiaochuan3   

  1. 1. Electric Power Dispatching & Control Center, State Grid Qingdao Power Supply Company, Qingdao 266002, China;
    2. Electric Power Dispatching & Control Center, State Grid Shandong Electric Power Company, Jinan 250001, China;
    3. School of Computer Science and Technology, Shandong University(Qingdao), Qingdao 266237, China
  • Received:2020-08-31 Published:2021-04-08
  • Supported by:
    Science and Technology Project of State Grid Shandong Electric Power Company (5206021900TW)

摘要: 厂站接线图中电气元件的拓扑关系是厂站接线图自动生成技术所需的核心数据。目前,已知的厂站接线图自动生成技术仍然依靠人工获取图中的拓扑关系。通过利用基于深度学习的目标检测技术与传统的计算机图像处理技术相结合的方式,能够实现厂站接线图拓扑关系检测。首先,利用基于深度学习的目标检测方法对电气元件进行识别,并利用计算机图像处理技术对标量格式接线图进行预处理,完成电气元件与连接线的分割。然后,利用轮廓跟踪算法对连接线连通区域进行检测标记。最后,根据获取的电气元件信息与连接线信息获取图纸的拓扑关系。采用国家电网有限公司提供的数据集,并设计了对比实验,验证了所提方法的有效性。

关键词: 厂站接线图, 拓扑关系, 深度学习, 目标检测, 轮廓跟踪

Abstract: Topological relation of electrical components is the core-data required by substation wiring diagram automatic generation technology. At present, known technologies still rely much on artificial access to topological relations. By the combination of deep learning based object detection technology and traditional computer image processing technology, topological relation can be detected automatically. Firstly, to segment the electrical components and connection lines, deep learning based object detection technology was used to identify the electrical components, and the image processing technology was used to preprocess the scalar-format wiring diagram of power plants. Secondly, a contour tracking algorithm was adopted to detect and mark the connected area of the connection lines. Finally, the topological relation of the drawing was acquired according to the obtained information of electrical components and connection lines. Comparative experiments based on a dataset released by the State Grid Corporation of China indicate the effectiveness of the proposed method.

Key words: substation wiring diagram, topological relation, deep learning, object detection, contour tracking

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