Cable Diagnostics With Power Line Modems for Smart Grid Monitoring

Remote monitoring of electrical cable conditions is an essential characteristic of the next-generation smart grid, which features the ability to consistently surveil and control the grid infrastructure. In this paper, we propose a technique that harnesses power line modems (PLMs) for monitoring cabl...

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Main Authors: Yinjia Huo, Gautham Prasad, Lazar Atanackovic, Lutz Lampe, Victor C. M. Leung
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8704721/
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spelling doaj-7a72fe67001c4530a9b17ea7729e7fc92021-03-29T22:49:25ZengIEEEIEEE Access2169-35362019-01-017602066022010.1109/ACCESS.2019.29145808704721Cable Diagnostics With Power Line Modems for Smart Grid MonitoringYinjia Huo0Gautham Prasad1Lazar Atanackovic2Lutz Lampe3https://orcid.org/0000-0002-6583-1978Victor C. M. Leung4Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, CanadaDepartment of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, CanadaDepartment of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, CanadaDepartment of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, CanadaDepartment of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, CanadaRemote monitoring of electrical cable conditions is an essential characteristic of the next-generation smart grid, which features the ability to consistently surveil and control the grid infrastructure. In this paper, we propose a technique that harnesses power line modems (PLMs) for monitoring cable health. We envisage that all or most of these PLMs have already been deployed for data communication purposes and focus on the distribution grid or neighborhood area networks in the smart grid. For such a setting, we propose a machine learning (ML)-based framework for automatic cable diagnostics by continuously monitoring the cable status to identify, assess, and locate possible degradations. As part of our technique, we also synthesize the state-of-the-art reflectometry methods within the PLMs to extract beneficial features for the effective performance of our proposed ML solution. The simulation results demonstrate the effectiveness of our solution under different aging conditions and varying load configurations. Finally, we reflect on our proposed diagnostics method by evaluating its robustness and comparing it with existing alternatives.https://ieeexplore.ieee.org/document/8704721/Smart grid monitoringcable diagnosticsagingpower line communicationsreflectometrymachine learning
collection DOAJ
language English
format Article
sources DOAJ
author Yinjia Huo
Gautham Prasad
Lazar Atanackovic
Lutz Lampe
Victor C. M. Leung
spellingShingle Yinjia Huo
Gautham Prasad
Lazar Atanackovic
Lutz Lampe
Victor C. M. Leung
Cable Diagnostics With Power Line Modems for Smart Grid Monitoring
IEEE Access
Smart grid monitoring
cable diagnostics
aging
power line communications
reflectometry
machine learning
author_facet Yinjia Huo
Gautham Prasad
Lazar Atanackovic
Lutz Lampe
Victor C. M. Leung
author_sort Yinjia Huo
title Cable Diagnostics With Power Line Modems for Smart Grid Monitoring
title_short Cable Diagnostics With Power Line Modems for Smart Grid Monitoring
title_full Cable Diagnostics With Power Line Modems for Smart Grid Monitoring
title_fullStr Cable Diagnostics With Power Line Modems for Smart Grid Monitoring
title_full_unstemmed Cable Diagnostics With Power Line Modems for Smart Grid Monitoring
title_sort cable diagnostics with power line modems for smart grid monitoring
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2019-01-01
description Remote monitoring of electrical cable conditions is an essential characteristic of the next-generation smart grid, which features the ability to consistently surveil and control the grid infrastructure. In this paper, we propose a technique that harnesses power line modems (PLMs) for monitoring cable health. We envisage that all or most of these PLMs have already been deployed for data communication purposes and focus on the distribution grid or neighborhood area networks in the smart grid. For such a setting, we propose a machine learning (ML)-based framework for automatic cable diagnostics by continuously monitoring the cable status to identify, assess, and locate possible degradations. As part of our technique, we also synthesize the state-of-the-art reflectometry methods within the PLMs to extract beneficial features for the effective performance of our proposed ML solution. The simulation results demonstrate the effectiveness of our solution under different aging conditions and varying load configurations. Finally, we reflect on our proposed diagnostics method by evaluating its robustness and comparing it with existing alternatives.
topic Smart grid monitoring
cable diagnostics
aging
power line communications
reflectometry
machine learning
url https://ieeexplore.ieee.org/document/8704721/
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AT lutzlampe cablediagnosticswithpowerlinemodemsforsmartgridmonitoring
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