|本期目录/Table of Contents|

[1]纪鹏志,董 振,付开强,等.配电线路接点温度远程监测与预测系统[J].工业仪表与自动化装置,2022,(01):76-81.[doi:10.19950/j.cnki.cn61-1121/th.2022.01.016]
 JI Pengzhi,DONG Zhen,FU Kaiqiang,et al.Distribution line contact temperature remote monitoring and prediction system[J].Industrial Instrumentation & Automation,2022,(01):76-81.[doi:10.19950/j.cnki.cn61-1121/th.2022.01.016]
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配电线路接点温度远程监测与预测系统

《工业仪表与自动化装置》[ISSN:1000-0682/CN:61-1121/TH]

卷:
期数:
2022年01期
页码:
76-81
栏目:
出版日期:
2022-02-15

文章信息/Info

Title:
Distribution line contact temperature remote monitoring and prediction system
文章编号:
1000-0682(2022)01-0000-00
作者:
纪鹏志1董 振1付开强1马骁雨2
1.国网济宁供电公司 山东 济宁272000;
2.济南大学 自动化与电气工程学院 山东 济南250022
Author(s):
JI Pengzhi1DONG Zhen1FU Kaiqiang1MA Xiaoyu2
1.State Grid Corporation Jining Electric Power Company, Jining 272000, China;
2.College of Automation & Electrical Engineering, Unversity of Jinan 250022,China
关键词:
配电线路接点温度监测差分自回归移动平均模型预测
Keywords:
distribution lines contact temperature monitor ARIMA forecast
分类号:
TP277
DOI:
10.19950/j.cnki.cn61-1121/th.2022.01.016
文献标志码:
A
摘要:
为了能够远程实时地监测配电线路中的T型接点、接续点温度,并且掌握接点温度未来的变化趋势,保障配电网安全运行,设计了一种配电线路接点温度远程监测与预测系统。该系统按感知层、网络层、平台层与应用层的顺序,由下而上逐层进行设计与开发。终端节点部署在线路接点处,以非接触式红外测温的方法获取温度数据,并通过LoRa技术将温度数据传输至汇聚节点,汇聚节点汇总下属所有终端节点的温度数据后通过4G网络上传至阿里云端,上位机从阿里云中获取温度数据并建立差分自回归移动平均模型(ARIMA)来预测接点温度的变化趋势,同时手机APP端同步显示接点温度的监测数据。测试结果表明,系统可以实现远程监测接点温度,ARIMA模型可较为精准的预测未来的温度值,提高了测温效率,具有较强的实用性。
Abstract:
In order to monitor the temperature of T-contact and connection point in distribution line remotely and in real time, grasp the future change trend of contact temperature, and ensure the safe operation of distribution network, a remote monitoring and prediction system of distribution line contact temperature is designed. The system is designed and developed layer by layer from bottom to top according to the order of perception layer, network layer, platform layer and application layer. The terminal node is deployed at the line contact, obtains the temperature data by non-contact infrared temperature measurement, and transmits the temperature data to the sink node through LoRa technology. The sink node summarizes the temperature data of all subordinate terminal nodes and uploads it to Alibaba cloud through 4G network, The upper computer obtains temperature data from Alibaba cloud and establishes ARIMA model to predict the change trend of contact temperature. At the same time, the mobile phone app synchronously displays the monitoring data of contact temperature. The test results show that the system can remotely monitor the contact temperature, and ARIMA model can accurately predict the future temperature value, improve the temperature measurement efficiency and have strong practicability.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2021-09-03

作者简介:
纪鹏志(1978),男,山东泰安人,高级工程师,主要研究方向为智能电网。
更新日期/Last Update: 1900-01-01