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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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/ |
work_keys_str_mv |
AT yinjiahuo cablediagnosticswithpowerlinemodemsforsmartgridmonitoring AT gauthamprasad cablediagnosticswithpowerlinemodemsforsmartgridmonitoring AT lazaratanackovic cablediagnosticswithpowerlinemodemsforsmartgridmonitoring AT lutzlampe cablediagnosticswithpowerlinemodemsforsmartgridmonitoring AT victorcmleung cablediagnosticswithpowerlinemodemsforsmartgridmonitoring |
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1724190807225794560 |