面向电力通信配电屏数字化的人工智能识别关键方法

刘林; 杨志敏; 黄强; 杨经纬; 陈一童

doi:10.11959/j.issn.1000-0801.2025041

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面向电力通信配电屏数字化的人工智能识别关键方法

工程与应用 | 更新时间：2025-05-13

- 面向电力通信配电屏数字化的人工智能识别关键方法
- Key method of digitization of power distribution panel with artificial intelligence identification for power communication network
- 电信科学 2025年41卷第4期页码：176-190
- 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：
  
  [ "刘林（1994- ），男，博士，中国南方电网电力调度控制中心工程师，主要研究方向为人工智能在电力系统通信中的应用。" ]
  [ "杨志敏（1982- ），男，博士，中国南方电网电力调度控制中心正高级工程师，主要研究方向为电力通信运行及技术支持系统、人工智能在电力通信中的应用。" ]
  [ "黄强（1982- ），男，中国南方电网电力调度控制中心高级工程师，主要研究方向为电力通信运行及技术支持系统、电力通信电源管理。" ]
  [ "杨经纬（1997- ），男，中国南方电网电力调度控制中心工程师，主要研究方向为电力通信运行及人工智能在电力通信中的应用。" ]
  [ "陈一童（1995- ），男，中国南方电网电力调度控制中心工程师，主要研究方向为电力通信运行及技术支持系统。" ]
- 基金信息：
  
  南方电网重点科技项目(ZDKJXM20222029)
- DOI：10.11959/j.issn.1000-0801.2025041
  中图分类号： TM73
- 收稿日期：2024-11-14，
  
  修回日期：2024-12-17，
  
  纸质出版日期：2025-04-20
- 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

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