Key method of digitization of power distribution panel with artificial intelligence identification for power communication network

LIU Lin; YANG Zhimin; HUANG Qiang; YANG Jingwei; CHEN Yitong

doi:10.11959/j.issn.1000-0801.2025041

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Key method of digitization of power distribution panel with artificial intelligence identification for power communication network

Engineering and Application | 更新时间：2025-10-31

- Key method of digitization of power distribution panel with artificial intelligence identification for power communication network
- Telecommunications Science Vol. 41, Issue 4, Pages: 176-190(2025)
- 作者机构：
  
  中国南方电网电力调度控制中心，广东广州 510525
- 作者简介：
- 基金信息：
  
  The Key Technology Project of China Southern Power Grid(ZDKJXM20222029)
- DOI：10.11959/j.issn.1000-0801.2025041
  CLC： TM73
- Received：14 November 2024，
  
  Revised：2024-12-17，
  
  Published：20 April 2025
- 稿件说明：
移动端阅览
刘林,杨志敏,黄强等.面向电力通信配电屏数字化的人工智能识别关键方法[J].电信科学,2025,41(04):176-190.

LIU Lin,YANG Zhimin,HUANG Qiang,et al.Key method of digitization of power distribution panel with artificial intelligence identification for power communication network[J].Telecommunications Science,2025,41(04):176-190.
刘林,杨志敏,黄强等.面向电力通信配电屏数字化的人工智能识别关键方法[J].电信科学,2025,41(04):176-190. DOI： 10.11959/j.issn.1000-0801.2025041.

LIU Lin,YANG Zhimin,HUANG Qiang,et al.Key method of digitization of power distribution panel with artificial intelligence identification for power communication network[J].Telecommunications Science,2025,41(04):176-190. DOI： 10.11959/j.issn.1000-0801.2025041.

摘要

为了提高电力通信网的运维管理效率，研究利用人工智能技术的通信电源系统配电屏数字化方法，通过目标检测和文字识别来实现配电屏中各下级支路供电状态的实时监测。首先，提出了一个多层嵌套识别网络（multi-layer nested recognition network，MLNRN）架构，结合轻量化策略，降低该架构对终端设备的存算需求，高效准确地实现了配电屏供电状态的结构化输出。其次，针对图元检测任务提出了一个改进的YOLOv5网络，通过引入ConvNext Block和双向特征金字塔网络（bidirectional feature pyramid network，Bi-FPN）结构，提高了对状态灯等小目标的识别精度。最后，基于卷积递归神经网络-连接时态分类（convolutional recurrent neural network- connectionist temporal classification，CRNN-CTC）网络构建针对配电屏下级支路标签的文本识别模型，采用迁移学习和图像增广的策略，提升模型对多字符种类、不规范配电屏图像中文本识别的准确率。仿真实验表明，图像识别的平均准确率为97.2%，文本方法的识别准确率为92%，这验证了所提架构在配电屏数字化任务中的有效性和适用性，为实现电力通信网的视频巡检和智慧运维提供了有效的解决方案。

Abstract

To enhance the operational management efficiency of the power communication network

artificial intelligence technology was utilized to develop a digitalization method for distribution panels within the communication power system. By implementing object detection and text recognition

real-time monitoring of the power supply status for each subordinate branch in the distribution panel was achieved. Firstly

a multi-layer nested recognition network (MLNRN) architecture was proposed

which incorporated lightweight strategies to reduce the computational demands on terminal devices

allowing for efficient and accurate structured output of the distribution panel's power supply status. Secondly

an improved YOLOv5 network was introduced for the task of icon detection. By integrating ConvNext Block and bidirectional feature pynamid network (Bi-FPN) structures

the recognition accuracy for small targets

such as status lights

was significantly enhanced. Finally

a text recognition model targeting the labels of subordinate branches in the distribution panel was constructed on the basis of the convolutional recurrent neural network-connectionist temporal classification (CRNN-CTC). Transfer learning and image augmentation strategies were employed to improve recognition accuracy for text with multiple character types in non-standard distribution panel images. Simulation experiments demonstrate that the average accuracy of image recognition is 97.2%

whereas the accuracy of text recognition is 92%. These results validate the effectiveness and applicability of the proposed architecture in the digitalization of power distribution panel

which provides an effective solution for video inspection and intelligent maintenance in power communication networks.

关键词

Keywords

references

唐大钧 . 基于数字化通信技术的电力通信网的运维探究 [J ] . 中国宽带 , 2024 ( 10 ): 164 - 166 .

TANG D J . Research on operation and maintenance of electric power communication network based on digital communication technology [J ] . China BroadBand , 2024 ( 10 ): 164 - 166 .

郝青云 . 电力通信网中通信电源的典型故障案例分析 [J ] . 内蒙古科技与经济 , 2024 ( 15 ): 134 - 137 .

HAO Q Y . Analysis of typical fault cases of communication power supply in power communication network [J ] . Inner Mongolia Science Technology & Economy , 2024 ( 15 ): 134 - 137 .

李鑫 . 智能变电站数字化站用电源交直流系统分析 [J ] . 光源与照明 , 2024 ( 10 ): 192 - 194 .

LI X . Analysis of AC/DC system of digital power supply for smart substation [J ] . Lamps & Lighting , 2024 ( 10 ): 192 - 194 .

尚宇炜 , 周莉梅 , 马钊 , 等 . 数字化主动配电系统初探 [J ] . 中国电机工程学报 , 2022 , 42 ( 5 ): 1760 - 1773 .

SHANG Y W , ZHOU L M , MA Z , et al . Preliminary study of digitalized active distribution systems [J ] . Proceedings of the CSEE , 2022 , 42 ( 5 ): 1760 - 1773 .

蒲天骄 , 赵琦 , 王新迎 . 电力人工智能技术研究框架、应用现状及展望 [J ] . 电网技术 , 2025 : 1 - 21 .

PU T J , ZHAO Q , WANG X Y . Technology framework, application status, and prospects on electric power artificial intelligence [J ] . Power System Technology , 2025 : 1 - 21 .

李雪峰 , 刘海莹 , 刘高华 , 等 . 基于深度学习的输电线路销钉缺陷检测 [J ] . 电网技术 , 2021 , 45 ( 8 ): 2988 - 2995 .

LI X F , LIU H Y , LIU G H , et al . Transmission line pin defect detection based on deep learning [J ] . Power System Technology , 2021 , 45 ( 8 ): 2988 - 2995 .

颜宏文 , 陈金鑫 . 基于改进YOLOv3的绝缘子串定位与状态识别方法 [J ] . 高电压技术 , 2020 , 46 ( 2 ): 423 - 432 .

YAN H W , CHEN J X . Insulator string positioning and state recognition method based on improved YOLOv3 algorithm [J ] . High Voltage Engineering , 2020 , 46 ( 2 ): 423 - 432 .

刘思言 , 王博 , 高昆仑 , 等 . 基于R-FCN的航拍巡检图像目标检测方法 [J ] . 电力系统自动化 , 2019 , 43 ( 13 ): 162 - 168 .

LIU S Y , WANG B , GAO K L , et al . Object detection method for aerial inspection image based on region-based fully convolutional network [J ] . Automation of Electric Power Systems , 2019 , 43 ( 13 ): 162 - 168 .

REDMON J , DIVVALA S , GIRSHICK R , et al . You only look once: unified, real-time object detection [C ] // Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE Press , 2016 : 779 - 788 .

陈嘉琛 , 俞曜辰 , 陈中 , 等 . 基于改进YOLOv3的输电线路缺陷识别方法 [J ] . 南方电网技术 , 2021 , 15 ( 1 ): 114 - 120 .

CHEN J C , YU Y C , CHEN Z , et al . An improved method for defect identification of transmission lines based on YOLOv3 [J ] . Southern Power System Technology , 2021 , 15 ( 1 ): 114 - 120 .

缪希仁 , 林志成 , 江灏 , 等 . 基于深度卷积神经网络的输电线路防鸟刺部件识别与故障检测 [J ] . 电网技术 , 2021 , 45 ( 1 ): 126 - 133 .

MIAO X R , LIN Z C , JIANG H , et al . Fault detection of power tower anti-bird spurs based on deep convolutional neural network [J ] . Power System Technology , 2021 , 45 ( 1 ): 126 - 133 .

蔡正奕 , 赵杰煜 , 朱峰 . 融合图像特征的单阶段点云目标检测 [J ] . 计算机工程与应用 , 2023 , 59 ( 9 ): 140 - 149 .

CAI Z Y , ZHAO J Y , ZHU F . Single-stage object detection with fusion of point cloud and image feature [J ] . Computer Engineering and Applications , 2023 , 59 ( 9 ): 140 - 149 .

黄悦华 , 刘恒冲 , 陈庆 , 等 . 基于USRNet与改进YOLOv5x的输电线路绝缘子故障检测方法 [J ] . 高电压技术 , 2022 , 48 ( 9 ): 3437 - 3446 .

HUANG Y H , LIU H C , CHEN Q , et al . Transmission line insulator fault detection method based on USRNet and improved YOLOv5x [J ] . High Voltage Engineering , 2022 , 48 ( 9 ): 3437 - 3446 .

肖粲俊 , 潘睿志 , 李超 , 等 . 基于改进YOLOv5s绝缘子缺陷检测技术研究 [J ] . 电子测量技术 , 2022 , 45 ( 24 ): 137 - 144 .

XIAO C J , PAN R Z , LI C , et al . Research on defect detection technology based on improved YOLOv5s insulator [J ] . Electronic Measurement Technology , 2022 , 45 ( 24 ): 137 - 144 .

何锦强 , 李锐海 , 李昊 , 等 . 基于Yolo v5与Grabcut的架空线路绝缘子可见光图像自动识别与分割方法 [J ] . 南方电网技术 , 2023 , 17 ( 6 ): 128 - 135 .

HE J Q , LI R H , LI H , et al . Visible light image automatic recognition and segmentation method for overhead power line insulators based on Yolo v5 and Grabcut [J ] . Southern Power System Technology , 2023 , 17 ( 6 ): 128 - 135 .

李新海 , 曾令诚 , 卢泳茵 , 等 . 一种小样本条件下的绝缘子外套裂纹缺陷图像识别方法 [J ] . 南方电网技术 , 2021 , 15 ( 12 ): 86 - 94 .

LI X H , ZENG L C , LU Y Y , et al . An image recognition method for insulator jacket crack defects under small sample conditions [J ] . Southern Power System Technology , 2021 , 15 ( 12 ): 86 - 94 .

王媛彬 , 李媛媛 , 段誉 , 等 . 基于轻量骨干网络和注意力结构的变电设备红外图像识别 [J ] . 电网技术 , 2023 , 47 ( 10 ): 4358 - 4369 .

WANG Y B , LI Y Y , DUAN Y , et al . Infrared image recognition of substation equipment based on lightweight backbone network and attention mechanism [J ] . Power System Technology , 2023 , 47 ( 10 ): 4358 - 4369 .

薛昂 , 姜恩宇 , 张文涛 , 等 . 基于窗口自注意力网络与YOLOv5融合的输电线路通道异物检测 [J ] . 上海交通大学学报 , 2025 : 1 - 22 .

XUE A , JIANG E Y , ZHANG W T , et al . Detection of foreign bodies in transmission line channels based on the fusion of swin transformer and YOLOv5 [J ] . Journal of Shanghai Jiao Tong University , 2025 : 1 - 22 .

王梓耀 , 罗庆全 , 萧文聪 , 等 . 电网厂站接线图人工智能识别关键方法 [J ] . 电力系统自动化 , 2023 , 47 ( 2 ): 115 - 124 .

WANG Z Y , LUO Q Q , XIAO W C , et al . Key method of wiring diagram recognition with artificial intelligence for power plant and substation [J ] . Automation of Electric Power Systems , 2023 , 47 ( 2 ): 115 - 124 .

张彦龙 , 许丹 , 曹晖 , 等 . 基于多阶段协同的电网厂站接线图智能识别 [J ] . 电网技术 , 2024 , 48 ( 10 ): 4341 - 4350 .

ZHANG Y L , XU D , CAO H , et al . Intelligent recognition of power grid station wiring diagrams based on multi-stage collaboration [J ] . Power System Technology , 2024 , 48 ( 10 ): 4341 - 4350 .

曹倩倩 . 电力铭牌图像中的文本检测与识别方法研究 [D ] . 南京 : 南京理工大学 , 2020 .

CAO Q Q . Research on text detection and recognition method in power nameplate image [D ] . Nanjing : Nanjing University of Science and Technology , 2020 .

WOO S , DEBNATH S , HU R H , et al . ConvNeXt V2: co-designing and scaling ConvNets with masked autoencoders [C ] // Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE Press , 2023 : 16133 - 16142 .

LIU Z , MAO H Z , WU C Y , et al . A ConvNet for the 2020s [C ] // Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Piscataway : IEEE Press , 2022 : 11966 - 11976 .

周乐 . 基于YOLO v4以及CRNN+CTC算法的中文车牌识别系统 [D ] . 南京 : 南京邮电大学 , 2022 .

ZHOU L . Chinese license plate recognition system based on YOLO v4 and CRNN+CTC algorithm [D ] . Nanjing : Nanjing University of Posts and Telecommunications , 2022 .

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