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ERIRMS：低压架空输电线路物联网辅助远程监测系统可靠性评估

ERIRMS Evaluation of the Reliability of IoT-Aided Remote Monitoring Systems of Low-Voltage Overhead Transmission Lines.

作者信息

Khujamatov Halimjon, Davronbekov Dilmurod, Khayrullaev Alisher, Abdullaev Mirjamol, Mukhiddinov Mukhriddin, Cho Jinsoo

机构信息

Department of Computer Engineering, Gachon University, Seognam-daero, Sujeong-gu, Seongnam-si 1342, Republic of Korea.

Department of Mobile Communication Technologies, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Tashkent 100200, Uzbekistan.

出版信息

Sensors (Basel). 2024 Sep 14;24(18):5970. doi: 10.3390/s24185970.

DOI:10.3390/s24185970
PMID:39338715
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11435488/
Abstract

Researchers have studied instances of power line technical failures, the significant rise in the energy loss index in the line connecting the distribution transformer and consumer meters, and the inability to control unauthorized line connections. New, innovative, and scientific approaches are required to address these issues while enhancing the reliability and efficiency of electricity supply. This study evaluates the reliability of Internet of Things (IoT)-aided remote monitoring systems specifically designed for a low-voltage overhead transmission line. Many methods of analysis and comparison have been employed to examine the reliability of wireless sensor devices used in real-time remote monitoring. A reliability model was developed to evaluate the reliability of the monitoring system in various situations. Based on the developed models, it was found that the reliability indicators of the proposed monitoring system were 98% in 1 month. In addition, it has been proven that the reliability of the system remains high even when an optional sensor in the network fails. This study investigates various IoT technologies, their integration into monitoring systems, and their effectiveness in enhancing the reliability and efficiency of electrical transmission infrastructure. The analysis includes data from field deployments, case studies, and simulations to assess performance metrics, such as accuracy, latency, and fault detection capabilities.

摘要

研究人员已经研究了电力线路技术故障的实例、连接配电变压器和用户电表的线路中能量损耗指数的显著上升，以及无法控制未经授权的线路连接情况。需要新的、创新的和科学的方法来解决这些问题，同时提高电力供应的可靠性和效率。本研究评估了专门为低压架空输电线路设计的物联网（IoT）辅助远程监测系统的可靠性。已经采用了许多分析和比较方法来检验实时远程监测中使用的无线传感器设备的可靠性。开发了一个可靠性模型来评估监测系统在各种情况下的可靠性。基于所开发的模型，发现所提出的监测系统在1个月内的可靠性指标为98%。此外，已经证明即使网络中的一个可选传感器出现故障，系统的可靠性仍然很高。本研究调查了各种物联网技术、它们在监测系统中的集成，以及它们在提高输电基础设施的可靠性和效率方面的有效性。分析包括来自现场部署、案例研究和模拟的数据，以评估诸如准确性、延迟和故障检测能力等性能指标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/006c931b7d41/sensors-24-05970-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/480753128c00/sensors-24-05970-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/211886a64ea2/sensors-24-05970-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/b896fcf76bbb/sensors-24-05970-g004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/363d0151552e/sensors-24-05970-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/e767c6de31f9/sensors-24-05970-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/7f5ee223d554/sensors-24-05970-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/09e1d5af6f92/sensors-24-05970-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/739a0d130b4f/sensors-24-05970-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/923db5dff149/sensors-24-05970-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/5c0390e7585a/sensors-24-05970-g013a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/006c931b7d41/sensors-24-05970-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/480753128c00/sensors-24-05970-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/883ee4d0255c/sensors-24-05970-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/211886a64ea2/sensors-24-05970-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/b896fcf76bbb/sensors-24-05970-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/e71f3df99ddf/sensors-24-05970-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/363d0151552e/sensors-24-05970-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/e767c6de31f9/sensors-24-05970-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/7f5ee223d554/sensors-24-05970-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/09e1d5af6f92/sensors-24-05970-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/739a0d130b4f/sensors-24-05970-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/923db5dff149/sensors-24-05970-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/b49812e7f715/sensors-24-05970-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/5c0390e7585a/sensors-24-05970-g013a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8108/11435488/006c931b7d41/sensors-24-05970-g014.jpg

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