Loading…

An ML-based Strategy to Identify Insulation Degradation in High Voltage Capacitive Bushings

Real-time monitoring of the electric power system has become essential for the efficient operation of the Smart Grid. In this scenario, the power transformer is the core equipment of a substation, and the proper operation of this equipment is of great importance for the power supply. The component r...

Full description

Saved in:
Bibliographic Details
Main Authors: Nacano, Karine M., Pellenz, Marcelo E., Rambo, Marcos Vinicio Haas, Jamhour, Edgard, Zambenedetti, Voldi C., Chueiri, Ivan J., Benetti, Daniel
Format: Conference Proceeding
Language:English
Subjects:
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 262
container_issue
container_start_page 256
container_title
container_volume
creator Nacano, Karine M.
Pellenz, Marcelo E.
Rambo, Marcos Vinicio Haas
Jamhour, Edgard
Zambenedetti, Voldi C.
Chueiri, Ivan J.
Benetti, Daniel
description Real-time monitoring of the electric power system has become essential for the efficient operation of the Smart Grid. In this scenario, the power transformer is the core equipment of a substation, and the proper operation of this equipment is of great importance for the power supply. The component responsible for terminal insulation in the power transformer is the bushing. As the bushing's lifetime depends on many factors, including the power demands, the manufacturing method, and the received stresses, online monitoring systems of these components are increasingly being used. An efficient and reliable monitoring system for identifying bushing's problems can reduce maintenance costs. It is possible to reduce the number of shutdowns for inspections and offline tests, and the risks of accidents caused by transformer explosions. Online monitoring systems for capacitive bushings are susceptible to acquisition circuit inaccuracies, noises, and interferences. In addition, bushing behavior can change due to temperature and humidity conditions. These operational parameters can cause fluctuations in online monitoring measurements and represent a challenge for correctly identifying bushing anomalies or degradations. This paper evaluates different machine learning (ML) approaches to identify anomalies in capacitive bushings. We propose the proper selection of features and the most efficient ML strategy to detect anomalies. We based the study on measured data from power transformers under normal and anomalous operation conditions.
doi_str_mv 10.1109/CCECE49351.2022.9918329
format conference_proceeding
fullrecord <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_9918329</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9918329</ieee_id><sourcerecordid>9918329</sourcerecordid><originalsourceid>FETCH-LOGICAL-i133t-dcebd7eff7c39d0ff75ca43041dbd254bf9834e8b6fe940fb381099449446a2c3</originalsourceid><addsrcrecordid>eNotkF1LwzAYhaMguE1_gRfmD7Tm423aXM44XWHihR83Xoy0edNFZjuaTNi_d7DBgedcPXAOIfec5Zwz_WDMwixAy4LnggmRa80rKfQFmXKlCqhACnFJJqIoVVYyUNdkGuMPYwwqBRPyPe_p6yprbERH39NoE3YHmgZaO-xT8Ada93G_tSkMPX3CbrTu1ENPl6Hb0K9hm2yH1NidbUMKf0gf93ET-i7ekCtvtxFvz5yRz-fFh1lmq7eX2sxXWeBSpsy12LgSvS9bqR07smgtSAbcNU4U0HhdScCqUR41MN_I6jhcAxyjrGjljNydvAER17sx_NrxsD4fIf8B80BUBQ</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>An ML-based Strategy to Identify Insulation Degradation in High Voltage Capacitive Bushings</title><source>IEEE Xplore All Conference Series</source><creator>Nacano, Karine M. ; Pellenz, Marcelo E. ; Rambo, Marcos Vinicio Haas ; Jamhour, Edgard ; Zambenedetti, Voldi C. ; Chueiri, Ivan J. ; Benetti, Daniel</creator><creatorcontrib>Nacano, Karine M. ; Pellenz, Marcelo E. ; Rambo, Marcos Vinicio Haas ; Jamhour, Edgard ; Zambenedetti, Voldi C. ; Chueiri, Ivan J. ; Benetti, Daniel</creatorcontrib><description>Real-time monitoring of the electric power system has become essential for the efficient operation of the Smart Grid. In this scenario, the power transformer is the core equipment of a substation, and the proper operation of this equipment is of great importance for the power supply. The component responsible for terminal insulation in the power transformer is the bushing. As the bushing's lifetime depends on many factors, including the power demands, the manufacturing method, and the received stresses, online monitoring systems of these components are increasingly being used. An efficient and reliable monitoring system for identifying bushing's problems can reduce maintenance costs. It is possible to reduce the number of shutdowns for inspections and offline tests, and the risks of accidents caused by transformer explosions. Online monitoring systems for capacitive bushings are susceptible to acquisition circuit inaccuracies, noises, and interferences. In addition, bushing behavior can change due to temperature and humidity conditions. These operational parameters can cause fluctuations in online monitoring measurements and represent a challenge for correctly identifying bushing anomalies or degradations. This paper evaluates different machine learning (ML) approaches to identify anomalies in capacitive bushings. We propose the proper selection of features and the most efficient ML strategy to detect anomalies. We based the study on measured data from power transformers under normal and anomalous operation conditions.</description><identifier>EISSN: 2576-7046</identifier><identifier>EISBN: 1665484322</identifier><identifier>EISBN: 9781665484329</identifier><identifier>DOI: 10.1109/CCECE49351.2022.9918329</identifier><language>eng</language><publisher>IEEE</publisher><subject>anomaly detection ; Behavioral sciences ; bushing ; Degradation ; Insulators ; Machine learning ; Measurement ; online monitoring ; power transformer ; smart grid ; Temperature measurement ; Temperature sensors</subject><ispartof>2022 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 2022, p.256-262</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9918329$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,23930,23931,25140,27925,54555,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9918329$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Nacano, Karine M.</creatorcontrib><creatorcontrib>Pellenz, Marcelo E.</creatorcontrib><creatorcontrib>Rambo, Marcos Vinicio Haas</creatorcontrib><creatorcontrib>Jamhour, Edgard</creatorcontrib><creatorcontrib>Zambenedetti, Voldi C.</creatorcontrib><creatorcontrib>Chueiri, Ivan J.</creatorcontrib><creatorcontrib>Benetti, Daniel</creatorcontrib><title>An ML-based Strategy to Identify Insulation Degradation in High Voltage Capacitive Bushings</title><title>2022 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)</title><addtitle>CCECE</addtitle><description>Real-time monitoring of the electric power system has become essential for the efficient operation of the Smart Grid. In this scenario, the power transformer is the core equipment of a substation, and the proper operation of this equipment is of great importance for the power supply. The component responsible for terminal insulation in the power transformer is the bushing. As the bushing's lifetime depends on many factors, including the power demands, the manufacturing method, and the received stresses, online monitoring systems of these components are increasingly being used. An efficient and reliable monitoring system for identifying bushing's problems can reduce maintenance costs. It is possible to reduce the number of shutdowns for inspections and offline tests, and the risks of accidents caused by transformer explosions. Online monitoring systems for capacitive bushings are susceptible to acquisition circuit inaccuracies, noises, and interferences. In addition, bushing behavior can change due to temperature and humidity conditions. These operational parameters can cause fluctuations in online monitoring measurements and represent a challenge for correctly identifying bushing anomalies or degradations. This paper evaluates different machine learning (ML) approaches to identify anomalies in capacitive bushings. We propose the proper selection of features and the most efficient ML strategy to detect anomalies. We based the study on measured data from power transformers under normal and anomalous operation conditions.</description><subject>anomaly detection</subject><subject>Behavioral sciences</subject><subject>bushing</subject><subject>Degradation</subject><subject>Insulators</subject><subject>Machine learning</subject><subject>Measurement</subject><subject>online monitoring</subject><subject>power transformer</subject><subject>smart grid</subject><subject>Temperature measurement</subject><subject>Temperature sensors</subject><issn>2576-7046</issn><isbn>1665484322</isbn><isbn>9781665484329</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2022</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotkF1LwzAYhaMguE1_gRfmD7Tm423aXM44XWHihR83Xoy0edNFZjuaTNi_d7DBgedcPXAOIfec5Zwz_WDMwixAy4LnggmRa80rKfQFmXKlCqhACnFJJqIoVVYyUNdkGuMPYwwqBRPyPe_p6yprbERH39NoE3YHmgZaO-xT8Ada93G_tSkMPX3CbrTu1ENPl6Hb0K9hm2yH1NidbUMKf0gf93ET-i7ekCtvtxFvz5yRz-fFh1lmq7eX2sxXWeBSpsy12LgSvS9bqR07smgtSAbcNU4U0HhdScCqUR41MN_I6jhcAxyjrGjljNydvAER17sx_NrxsD4fIf8B80BUBQ</recordid><startdate>20220918</startdate><enddate>20220918</enddate><creator>Nacano, Karine M.</creator><creator>Pellenz, Marcelo E.</creator><creator>Rambo, Marcos Vinicio Haas</creator><creator>Jamhour, Edgard</creator><creator>Zambenedetti, Voldi C.</creator><creator>Chueiri, Ivan J.</creator><creator>Benetti, Daniel</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>20220918</creationdate><title>An ML-based Strategy to Identify Insulation Degradation in High Voltage Capacitive Bushings</title><author>Nacano, Karine M. ; Pellenz, Marcelo E. ; Rambo, Marcos Vinicio Haas ; Jamhour, Edgard ; Zambenedetti, Voldi C. ; Chueiri, Ivan J. ; Benetti, Daniel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i133t-dcebd7eff7c39d0ff75ca43041dbd254bf9834e8b6fe940fb381099449446a2c3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2022</creationdate><topic>anomaly detection</topic><topic>Behavioral sciences</topic><topic>bushing</topic><topic>Degradation</topic><topic>Insulators</topic><topic>Machine learning</topic><topic>Measurement</topic><topic>online monitoring</topic><topic>power transformer</topic><topic>smart grid</topic><topic>Temperature measurement</topic><topic>Temperature sensors</topic><toplevel>online_resources</toplevel><creatorcontrib>Nacano, Karine M.</creatorcontrib><creatorcontrib>Pellenz, Marcelo E.</creatorcontrib><creatorcontrib>Rambo, Marcos Vinicio Haas</creatorcontrib><creatorcontrib>Jamhour, Edgard</creatorcontrib><creatorcontrib>Zambenedetti, Voldi C.</creatorcontrib><creatorcontrib>Chueiri, Ivan J.</creatorcontrib><creatorcontrib>Benetti, Daniel</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library Online</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Nacano, Karine M.</au><au>Pellenz, Marcelo E.</au><au>Rambo, Marcos Vinicio Haas</au><au>Jamhour, Edgard</au><au>Zambenedetti, Voldi C.</au><au>Chueiri, Ivan J.</au><au>Benetti, Daniel</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>An ML-based Strategy to Identify Insulation Degradation in High Voltage Capacitive Bushings</atitle><btitle>2022 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)</btitle><stitle>CCECE</stitle><date>2022-09-18</date><risdate>2022</risdate><spage>256</spage><epage>262</epage><pages>256-262</pages><eissn>2576-7046</eissn><eisbn>1665484322</eisbn><eisbn>9781665484329</eisbn><abstract>Real-time monitoring of the electric power system has become essential for the efficient operation of the Smart Grid. In this scenario, the power transformer is the core equipment of a substation, and the proper operation of this equipment is of great importance for the power supply. The component responsible for terminal insulation in the power transformer is the bushing. As the bushing's lifetime depends on many factors, including the power demands, the manufacturing method, and the received stresses, online monitoring systems of these components are increasingly being used. An efficient and reliable monitoring system for identifying bushing's problems can reduce maintenance costs. It is possible to reduce the number of shutdowns for inspections and offline tests, and the risks of accidents caused by transformer explosions. Online monitoring systems for capacitive bushings are susceptible to acquisition circuit inaccuracies, noises, and interferences. In addition, bushing behavior can change due to temperature and humidity conditions. These operational parameters can cause fluctuations in online monitoring measurements and represent a challenge for correctly identifying bushing anomalies or degradations. This paper evaluates different machine learning (ML) approaches to identify anomalies in capacitive bushings. We propose the proper selection of features and the most efficient ML strategy to detect anomalies. We based the study on measured data from power transformers under normal and anomalous operation conditions.</abstract><pub>IEEE</pub><doi>10.1109/CCECE49351.2022.9918329</doi><tpages>7</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier EISSN: 2576-7046
ispartof 2022 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), 2022, p.256-262
issn 2576-7046
language eng
recordid cdi_ieee_primary_9918329
source IEEE Xplore All Conference Series
subjects anomaly detection
Behavioral sciences
bushing
Degradation
Insulators
Machine learning
Measurement
online monitoring
power transformer
smart grid
Temperature measurement
Temperature sensors
title An ML-based Strategy to Identify Insulation Degradation in High Voltage Capacitive Bushings
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-24T12%3A51%3A39IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=An%20ML-based%20Strategy%20to%20Identify%20Insulation%20Degradation%20in%20High%20Voltage%20Capacitive%20Bushings&rft.btitle=2022%20IEEE%20Canadian%20Conference%20on%20Electrical%20and%20Computer%20Engineering%20(CCECE)&rft.au=Nacano,%20Karine%20M.&rft.date=2022-09-18&rft.spage=256&rft.epage=262&rft.pages=256-262&rft.eissn=2576-7046&rft_id=info:doi/10.1109/CCECE49351.2022.9918329&rft.eisbn=1665484322&rft.eisbn_list=9781665484329&rft_dat=%3Cieee_CHZPO%3E9918329%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i133t-dcebd7eff7c39d0ff75ca43041dbd254bf9834e8b6fe940fb381099449446a2c3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=9918329&rfr_iscdi=true