External insulation strength assessment of contaminated insulator based on acoustic emission
External insulation strength assessment of contaminated insulator is proposed in this paper using the acoustic emission signals generated when the polluted insulator flashover discharges. Systematic artificial contamination experiments were done. The acoustic emission signals generated from the poll...
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| creator | Li Zipin Li Hongling Wang Youyin |
| description | External insulation strength assessment of contaminated insulator is proposed in this paper using the acoustic emission signals generated when the polluted insulator flashover discharges. Systematic artificial contamination experiments were done. The acoustic emission signals generated from the polluted insulator were monitored by the sound monitoring device with high sensitivity. And the acoustic emission signals were analyzed. It shows that there is a relationship between the strength of filthy discharge and acoustic emission signals. The 14 features that can reflect acoustic emission signals of polluted insulator were extracted. Then a method of principal feature selection based on algorithm ReliefF is utilized. By using least squares support machine (LS-SVM), the classification model of is built. After analysis and comparison, LS-SVM model has a higher accuracy in t classification of different external insulation strength stages. |
| doi_str_mv | 10.1109/PEAM.2012.6612502 |
| format | Conference Proceeding |
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Systematic artificial contamination experiments were done. The acoustic emission signals generated from the polluted insulator were monitored by the sound monitoring device with high sensitivity. And the acoustic emission signals were analyzed. It shows that there is a relationship between the strength of filthy discharge and acoustic emission signals. The 14 features that can reflect acoustic emission signals of polluted insulator were extracted. Then a method of principal feature selection based on algorithm ReliefF is utilized. By using least squares support machine (LS-SVM), the classification model of is built. After analysis and comparison, LS-SVM model has a higher accuracy in t classification of different external insulation strength stages.</description><identifier>EISBN: 1457715996</identifier><identifier>EISBN: 9781457716003</identifier><identifier>EISBN: 9781457715990</identifier><identifier>EISBN: 1457716003</identifier><identifier>DOI: 10.1109/PEAM.2012.6612502</identifier><language>eng</language><publisher>IEEE</publisher><subject>Acoustic emission ; Contamination ; Discharges (electric) ; external insulation strength assessment ; Feature extraction ; insulator ; Insulators ; LS-SVM ; principal feature selection</subject><ispartof>2012 Power Engineering and Automation Conference, 2012, p.1-4</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/6612502$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>310,311,781,785,790,791,2059,27930,54925</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6612502$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Li Zipin</creatorcontrib><creatorcontrib>Li Hongling</creatorcontrib><creatorcontrib>Wang Youyin</creatorcontrib><title>External insulation strength assessment of contaminated insulator based on acoustic emission</title><title>2012 Power Engineering and Automation Conference</title><addtitle>PEAM</addtitle><description>External insulation strength assessment of contaminated insulator is proposed in this paper using the acoustic emission signals generated when the polluted insulator flashover discharges. Systematic artificial contamination experiments were done. The acoustic emission signals generated from the polluted insulator were monitored by the sound monitoring device with high sensitivity. And the acoustic emission signals were analyzed. It shows that there is a relationship between the strength of filthy discharge and acoustic emission signals. The 14 features that can reflect acoustic emission signals of polluted insulator were extracted. Then a method of principal feature selection based on algorithm ReliefF is utilized. By using least squares support machine (LS-SVM), the classification model of is built. After analysis and comparison, LS-SVM model has a higher accuracy in t classification of different external insulation strength stages.</description><subject>Acoustic emission</subject><subject>Contamination</subject><subject>Discharges (electric)</subject><subject>external insulation strength assessment</subject><subject>Feature extraction</subject><subject>insulator</subject><subject>Insulators</subject><subject>LS-SVM</subject><subject>principal feature selection</subject><isbn>1457715996</isbn><isbn>9781457716003</isbn><isbn>9781457715990</isbn><isbn>1457716003</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1j81KxDAUheNCUMd5AHGTF2i9SZukXQ7D-AMjupilMNymNxppU-nNgL69A46rw4HzfXCEuFFQKgXt3etm9VxqULq0VmkD-kxcqdo4p0zb2guxZP4EAOWsrZS5FG-b70xzwkHGxIcBc5yS5DxTes8fEpmJeaSU5RSkn1LGMSbM1P_Pp1l2yMd-xNBPB87RSxoj81F0Lc4DDkzLUy7E7n6zWz8W25eHp_VqW8QWckFgTWh6bb1zwSNiTbq26GzT1qB6TRTAY-gCND0pdLrtqtBp2xgwvlFNtRC3f9pIRPuvOY44_-xP_6tfrvJTww</recordid><startdate>201209</startdate><enddate>201209</enddate><creator>Li Zipin</creator><creator>Li Hongling</creator><creator>Wang Youyin</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201209</creationdate><title>External insulation strength assessment of contaminated insulator based on acoustic emission</title><author>Li Zipin ; Li Hongling ; Wang Youyin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-e065f8d26c77fcaaa4e246a7689401d2eef0cafbf08de1a729b3fb268505c8183</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Acoustic emission</topic><topic>Contamination</topic><topic>Discharges (electric)</topic><topic>external insulation strength assessment</topic><topic>Feature extraction</topic><topic>insulator</topic><topic>Insulators</topic><topic>LS-SVM</topic><topic>principal feature selection</topic><toplevel>online_resources</toplevel><creatorcontrib>Li Zipin</creatorcontrib><creatorcontrib>Li Hongling</creatorcontrib><creatorcontrib>Wang Youyin</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Li Zipin</au><au>Li Hongling</au><au>Wang Youyin</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>External insulation strength assessment of contaminated insulator based on acoustic emission</atitle><btitle>2012 Power Engineering and Automation Conference</btitle><stitle>PEAM</stitle><date>2012-09</date><risdate>2012</risdate><spage>1</spage><epage>4</epage><pages>1-4</pages><eisbn>1457715996</eisbn><eisbn>9781457716003</eisbn><eisbn>9781457715990</eisbn><eisbn>1457716003</eisbn><abstract>External insulation strength assessment of contaminated insulator is proposed in this paper using the acoustic emission signals generated when the polluted insulator flashover discharges. Systematic artificial contamination experiments were done. The acoustic emission signals generated from the polluted insulator were monitored by the sound monitoring device with high sensitivity. And the acoustic emission signals were analyzed. It shows that there is a relationship between the strength of filthy discharge and acoustic emission signals. The 14 features that can reflect acoustic emission signals of polluted insulator were extracted. Then a method of principal feature selection based on algorithm ReliefF is utilized. By using least squares support machine (LS-SVM), the classification model of is built. After analysis and comparison, LS-SVM model has a higher accuracy in t classification of different external insulation strength stages.</abstract><pub>IEEE</pub><doi>10.1109/PEAM.2012.6612502</doi><tpages>4</tpages></addata></record> |
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| ispartof | 2012 Power Engineering and Automation Conference, 2012, p.1-4 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Acoustic emission Contamination Discharges (electric) external insulation strength assessment Feature extraction insulator Insulators LS-SVM principal feature selection |
| title | External insulation strength assessment of contaminated insulator based on acoustic emission |
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