Study on Intelligent Operation and Inspection Technology of Substation Based on Edge Agent and Lightweight Deep Learning Model

YANG Dongdong; WANG Ruijue; ZHENG Zhihong; YANG Gang

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Study on Intelligent Operation and Inspection Technology of Substation Based on Edge Agent and Lightweight Deep Learning Model

更新时间：2025-06-18

- Study on Intelligent Operation and Inspection Technology of Substation Based on Edge Agent and Lightweight Deep Learning Model
- High Voltage Apparatus Pages: 1-7(2025)
- 作者机构：
  
  1.国网山西省电力公司电力科学研究院，太原　 030001
  2.国网山西省电力公司，太原　 030001
- 作者简介：
- 基金信息：
  
  Key Science and Technology Project of State Grid Shanxi Corporation(520530202002)
- DOI：
  CLC：
- Published：
- 稿件说明：
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YANG Dongdong, WANG Ruijue, ZHENG Zhihong, et al. Study on Intelligent Operation and Inspection Technology of Substation Based on Edge Agent and Lightweight Deep Learning Model[J/OL]. High voltage apparatus, 2025, 1-7.
DOI：

YANG Dongdong, WANG Ruijue, ZHENG Zhihong, et al. Study on Intelligent Operation and Inspection Technology of Substation Based on Edge Agent and Lightweight Deep Learning Model[J/OL]. High voltage apparatus, 2025, 1-7. DOI：

摘要

随着物联网和人工智能技术日趋发展，巡检机器人已在变电站开展研究并取得了较多成效，但因受现场地形、电磁等环境影响，以及监测对象安装位置的局限性，实用性存在一定不足，且受限于作业时间窗口，无法对重点设备重点对象实现实时在线监测。为此文中提出了以边缘代理集成轻量化深度学习算法作为变电站重要设备的运检补充手段。文中在基于开源的深度学习模型基础上进行了裁剪优化，采用了百度较为先进的飞桨Paddle Lite框架，实现了在边缘代理装置上集成部署，并以变压器油温表计读数识别为典型应用案例进行研究。实验结果验证了文章研究成果实现了在边缘代理设备有限的硬件资源下的集成部署深度学习模型，并满足了变压器油温表识别的准确率和性能的应用要求。研究成果可应用在变电站内对人员身份识别、违章违规作业、环境安全监控、仪表自动读数等智能运检业务领域，具备较好的先进性和推广性。

Abstract

With the development of Internet of things and artificial intelligence technology

patrol inspection robot has carried out research in substation and achieved many results. Due to the influence of the terrain and electromagnetic environment on the site

as well as the limitations of the installation location of the monitoring object

there are many deficiencies in the practicability of the inspection robot. So we propose to use the edge agent integrated lightweight deep learning algorithm as a supplementary means for the operation inspection of important substation equipment. Based on the open source in-depth learning model

we have carried out cutting optimization

adopted Baidu's advanced paddle Lite framework andtaken the reading recognition of transformer oil thermometer as a typical application case. The researching results can be widely used in the field of power grid intelligent operation inspection

such as personnel identification

illegal operations

environmental safety monitoring

automatic meter reading

etc. in substations. The research results of this paper have good progressiveness and popularization.

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references

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