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Sensing Surface Contamination of Metal Oxide Surge Arrester Through Resistive Leakage Current Signal Analysis by Mathematical Morphology
This paper presents an advanced method of sensing surface contamination of polymeric housed Metal Oxide Surge Arrester through resistive leakage current signal analysis. Surface condition of polymeric housed MOSAs often gets contaminated due to accumulation of dust and other pollutant. Accumulated p...
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Published in: | IEEE sensors journal 2020-08, Vol.20 (16), p.9460-9468 |
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creator | Das, Arup Kumar Ghosh, Banibrata Dalai, Sovan Chatterjee, Biswendu |
description | This paper presents an advanced method of sensing surface contamination of polymeric housed Metal Oxide Surge Arrester through resistive leakage current signal analysis. Surface condition of polymeric housed MOSAs often gets contaminated due to accumulation of dust and other pollutant. Accumulated pollutants can degrade the condition of the arrester due to overheating which may lead to explosion. Therefore, reliability of power system may get affected due to failure of MOSAs. Resistive leakage current analysis of MOSA is one of the conventional method for sensing surface contamination of surge arrester. In this article, Mathematical Morphology operator has been introduced to extract various features from the resistive part of leakage current signals measured at different surface contamination level. Further, the extracted features have been trained through Gaussian Naïve Bayes (GNB) and surface contamination level of MOSA has been identified through this classifier. Result shows that proposed technique provides satisfactory outcomes regarding condition monitoring of MOSA at different surface contamination level which in turn enhances the reliability of system. The proposed technique is generic in nature and well suited for any other similar kind of applications. |
doi_str_mv | 10.1109/JSEN.2020.2986677 |
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Surface condition of polymeric housed MOSAs often gets contaminated due to accumulation of dust and other pollutant. Accumulated pollutants can degrade the condition of the arrester due to overheating which may lead to explosion. Therefore, reliability of power system may get affected due to failure of MOSAs. Resistive leakage current analysis of MOSA is one of the conventional method for sensing surface contamination of surge arrester. In this article, Mathematical Morphology operator has been introduced to extract various features from the resistive part of leakage current signals measured at different surface contamination level. Further, the extracted features have been trained through Gaussian Naïve Bayes (GNB) and surface contamination level of MOSA has been identified through this classifier. Result shows that proposed technique provides satisfactory outcomes regarding condition monitoring of MOSA at different surface contamination level which in turn enhances the reliability of system. The proposed technique is generic in nature and well suited for any other similar kind of applications.</description><identifier>ISSN: 1530-437X</identifier><identifier>EISSN: 1558-1748</identifier><identifier>DOI: 10.1109/JSEN.2020.2986677</identifier><identifier>CODEN: ISJEAZ</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Arresters ; Condition monitoring ; Contamination ; Detection ; Failure analysis ; Feature extraction ; Gaussian Naïve Bayes ; Leakage current ; Leakage currents ; Mathematical analysis ; Mathematical morphology ; mathematicalmorphology ; Metal oxide surge arrester ; Metal oxides ; Morphology ; Overheating ; Pollutants ; Pollution measurement ; Signal analysis ; Surface contamination ; Surface morphology ; Surge arresters ; Surges ; System reliability</subject><ispartof>IEEE sensors journal, 2020-08, Vol.20 (16), p.9460-9468</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0001-5991-1237 ; 0000-0001-5367-2466 ; 0000-0001-9817-0435</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9060819$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,54796</link.rule.ids></links><search><creatorcontrib>Das, Arup Kumar</creatorcontrib><creatorcontrib>Ghosh, Banibrata</creatorcontrib><creatorcontrib>Dalai, Sovan</creatorcontrib><creatorcontrib>Chatterjee, Biswendu</creatorcontrib><title>Sensing Surface Contamination of Metal Oxide Surge Arrester Through Resistive Leakage Current Signal Analysis by Mathematical Morphology</title><title>IEEE sensors journal</title><addtitle>JSEN</addtitle><description>This paper presents an advanced method of sensing surface contamination of polymeric housed Metal Oxide Surge Arrester through resistive leakage current signal analysis. Surface condition of polymeric housed MOSAs often gets contaminated due to accumulation of dust and other pollutant. Accumulated pollutants can degrade the condition of the arrester due to overheating which may lead to explosion. Therefore, reliability of power system may get affected due to failure of MOSAs. Resistive leakage current analysis of MOSA is one of the conventional method for sensing surface contamination of surge arrester. In this article, Mathematical Morphology operator has been introduced to extract various features from the resistive part of leakage current signals measured at different surface contamination level. Further, the extracted features have been trained through Gaussian Naïve Bayes (GNB) and surface contamination level of MOSA has been identified through this classifier. Result shows that proposed technique provides satisfactory outcomes regarding condition monitoring of MOSA at different surface contamination level which in turn enhances the reliability of system. The proposed technique is generic in nature and well suited for any other similar kind of applications.</description><subject>Arresters</subject><subject>Condition monitoring</subject><subject>Contamination</subject><subject>Detection</subject><subject>Failure analysis</subject><subject>Feature extraction</subject><subject>Gaussian Naïve Bayes</subject><subject>Leakage current</subject><subject>Leakage currents</subject><subject>Mathematical analysis</subject><subject>Mathematical morphology</subject><subject>mathematicalmorphology</subject><subject>Metal oxide surge arrester</subject><subject>Metal oxides</subject><subject>Morphology</subject><subject>Overheating</subject><subject>Pollutants</subject><subject>Pollution measurement</subject><subject>Signal analysis</subject><subject>Surface contamination</subject><subject>Surface morphology</subject><subject>Surge arresters</subject><subject>Surges</subject><subject>System reliability</subject><issn>1530-437X</issn><issn>1558-1748</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNotz0tLw0AQAOAgCtbqDxAvC55T95F95FiCT1oLpoK3sEkmydY2WzeJmH_gz3ZLvcwMzDfDTBBcEzwjBMd3L-n964xiimc0VkJIeRJMCOcqJDJSp4ea4TBi8uM8uOi6DcYkllxOgt8U2s60NUoHV-kCUGLbXu9Mq3tjW2QrtIReb9Hqx5RwQDWguXPQ9eDQunF2qBv0Bp3pevMNaAH6U3uSDN60PUpN3frpuQ-jNygf0VL3Dez8-sI3ltbtG7u19XgZnFV628HVf54G7w_36-QpXKwen5P5IjQUsz6kXImCUFaWPMeca6poxHJChcAiKljBZUkikFApVuZclJIJRXGJZQ45L5Rm0-D2uHfv7Nfg_8g2dnD-vi6jEeWCUBrHXt0clQGAbO_MTrsxi7HAisTsD8Lebs4</recordid><startdate>20200815</startdate><enddate>20200815</enddate><creator>Das, Arup Kumar</creator><creator>Ghosh, Banibrata</creator><creator>Dalai, Sovan</creator><creator>Chatterjee, Biswendu</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0001-5991-1237</orcidid><orcidid>https://orcid.org/0000-0001-5367-2466</orcidid><orcidid>https://orcid.org/0000-0001-9817-0435</orcidid></search><sort><creationdate>20200815</creationdate><title>Sensing Surface Contamination of Metal Oxide Surge Arrester Through Resistive Leakage Current Signal Analysis by Mathematical Morphology</title><author>Das, Arup Kumar ; Ghosh, Banibrata ; Dalai, Sovan ; Chatterjee, Biswendu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i203t-2586c123dd5b055a28243b1266064c3c57d14e7ef83db56d736820d07beb5c8a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Arresters</topic><topic>Condition monitoring</topic><topic>Contamination</topic><topic>Detection</topic><topic>Failure analysis</topic><topic>Feature extraction</topic><topic>Gaussian Naïve Bayes</topic><topic>Leakage current</topic><topic>Leakage currents</topic><topic>Mathematical analysis</topic><topic>Mathematical morphology</topic><topic>mathematicalmorphology</topic><topic>Metal oxide surge arrester</topic><topic>Metal oxides</topic><topic>Morphology</topic><topic>Overheating</topic><topic>Pollutants</topic><topic>Pollution measurement</topic><topic>Signal analysis</topic><topic>Surface contamination</topic><topic>Surface morphology</topic><topic>Surge arresters</topic><topic>Surges</topic><topic>System reliability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Das, Arup Kumar</creatorcontrib><creatorcontrib>Ghosh, Banibrata</creatorcontrib><creatorcontrib>Dalai, Sovan</creatorcontrib><creatorcontrib>Chatterjee, Biswendu</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE/IET Electronic Library</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE sensors journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Das, Arup Kumar</au><au>Ghosh, Banibrata</au><au>Dalai, Sovan</au><au>Chatterjee, Biswendu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sensing Surface Contamination of Metal Oxide Surge Arrester Through Resistive Leakage Current Signal Analysis by Mathematical Morphology</atitle><jtitle>IEEE sensors journal</jtitle><stitle>JSEN</stitle><date>2020-08-15</date><risdate>2020</risdate><volume>20</volume><issue>16</issue><spage>9460</spage><epage>9468</epage><pages>9460-9468</pages><issn>1530-437X</issn><eissn>1558-1748</eissn><coden>ISJEAZ</coden><abstract>This paper presents an advanced method of sensing surface contamination of polymeric housed Metal Oxide Surge Arrester through resistive leakage current signal analysis. Surface condition of polymeric housed MOSAs often gets contaminated due to accumulation of dust and other pollutant. Accumulated pollutants can degrade the condition of the arrester due to overheating which may lead to explosion. Therefore, reliability of power system may get affected due to failure of MOSAs. Resistive leakage current analysis of MOSA is one of the conventional method for sensing surface contamination of surge arrester. In this article, Mathematical Morphology operator has been introduced to extract various features from the resistive part of leakage current signals measured at different surface contamination level. Further, the extracted features have been trained through Gaussian Naïve Bayes (GNB) and surface contamination level of MOSA has been identified through this classifier. Result shows that proposed technique provides satisfactory outcomes regarding condition monitoring of MOSA at different surface contamination level which in turn enhances the reliability of system. The proposed technique is generic in nature and well suited for any other similar kind of applications.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/JSEN.2020.2986677</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0001-5991-1237</orcidid><orcidid>https://orcid.org/0000-0001-5367-2466</orcidid><orcidid>https://orcid.org/0000-0001-9817-0435</orcidid></addata></record> |
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subjects | Arresters Condition monitoring Contamination Detection Failure analysis Feature extraction Gaussian Naïve Bayes Leakage current Leakage currents Mathematical analysis Mathematical morphology mathematicalmorphology Metal oxide surge arrester Metal oxides Morphology Overheating Pollutants Pollution measurement Signal analysis Surface contamination Surface morphology Surge arresters Surges System reliability |
title | Sensing Surface Contamination of Metal Oxide Surge Arrester Through Resistive Leakage Current Signal Analysis by Mathematical Morphology |
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