A neural network based method for leakage current prediction of polymeric insulators
This letter describes a neural network approach to the prediction of the leakage current (LC) on silicone rubber insulators exposed to salt-fog. The validity of the approach was examined by testing several insulators in a salt-fog chamber. Feed-forward back propagation was found as the best method a...
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| Veröffentlicht in: | IEEE transactions on power delivery 2006-01, Vol.21 (1), p.506-507 |
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| container_end_page | 507 |
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| container_title | IEEE transactions on power delivery |
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| creator | Jahromi, A.N. El-Hag, A.H. Jayaram, S.H. Cherney, E.A. Sanaye-Pasand, M. Mohseni, H. |
| description | This letter describes a neural network approach to the prediction of the leakage current (LC) on silicone rubber insulators exposed to salt-fog. The validity of the approach was examined by testing several insulators in a salt-fog chamber. Feed-forward back propagation was found as the best method among several training methods evaluated for the prediction of the LC. The predicted LC with this method has less than 12% error for the tested cases. |
| doi_str_mv | 10.1109/TPWRD.2005.858805 |
| format | Article |
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The validity of the approach was examined by testing several insulators in a salt-fog chamber. Feed-forward back propagation was found as the best method among several training methods evaluated for the prediction of the LC. The predicted LC with this method has less than 12% error for the tested cases.</description><identifier>ISSN: 0885-8977</identifier><identifier>EISSN: 1937-4208</identifier><identifier>DOI: 10.1109/TPWRD.2005.858805</identifier><identifier>CODEN: ITPDE5</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Aging ; Applied sciences ; Back propagation ; Chambers ; Degradation ; Electric, optical and optoelectronic circuits ; Electrical engineering. Electrical power engineering ; Electronics ; Errors ; Exact sciences and technology ; Feedforward systems ; Insulation life ; Insulator testing ; Insulators ; Leakage current ; neural network ; Neural networks ; Plastic insulation ; polymeric insulator ; Polymers ; Rubber ; salt-fog test ; Silicone rubber ; Training ; Various equipment and components</subject><ispartof>IEEE transactions on power delivery, 2006-01, Vol.21 (1), p.506-507</ispartof><rights>2006 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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The validity of the approach was examined by testing several insulators in a salt-fog chamber. Feed-forward back propagation was found as the best method among several training methods evaluated for the prediction of the LC. The predicted LC with this method has less than 12% error for the tested cases.</description><subject>Aging</subject><subject>Applied sciences</subject><subject>Back propagation</subject><subject>Chambers</subject><subject>Degradation</subject><subject>Electric, optical and optoelectronic circuits</subject><subject>Electrical engineering. Electrical power engineering</subject><subject>Electronics</subject><subject>Errors</subject><subject>Exact sciences and technology</subject><subject>Feedforward systems</subject><subject>Insulation life</subject><subject>Insulator testing</subject><subject>Insulators</subject><subject>Leakage current</subject><subject>neural network</subject><subject>Neural networks</subject><subject>Plastic insulation</subject><subject>polymeric insulator</subject><subject>Polymers</subject><subject>Rubber</subject><subject>salt-fog test</subject><subject>Silicone rubber</subject><subject>Training</subject><subject>Various equipment and components</subject><issn>0885-8977</issn><issn>1937-4208</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kUtLJDEUhYMo2D5-gLgJwsysqufmnSxFZ0ZBUIYWlyGmb2ZKqyttUoX476e0BWEWrs7ifufA5SPkiMGcMXDfFzd3v8_nHEDNrbIW1BaZMSdMIznYbTIDa1VjnTG7ZK_WBwCQ4GBGFqe0x7GEborhOZdHeh8qLukKh795SVMutMPwGP4gjWMp2A90XXDZxqHNPc2JrnP3ssLSRtr2dezCkEs9IDspdBUP33Of3P78sTi7aK6uf12enV41UVg1NC6Ci1pLB9yxCCg505Gn4LhDbtSSGS4TEyFItEpEKZEliYnH-2CsUCD2ybfN7rrkpxHr4Fdtjdh1occ8Vm-dZkZLZiby66ckt6CZ5m4CT_4DH_JY-ukLb7UWViqtJohtoFhyrQWTX5d2FcqLZ-Bfdfg3Hf5Vh9_omDpf3odDjaFLJfSxrR9FI6xzzk7c8YZrEfHjrLTkEsQ_gP-S6w</recordid><startdate>200601</startdate><enddate>200601</enddate><creator>Jahromi, A.N.</creator><creator>El-Hag, A.H.</creator><creator>Jayaram, S.H.</creator><creator>Cherney, E.A.</creator><creator>Sanaye-Pasand, M.</creator><creator>Mohseni, H.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Electrical power engineering</topic><topic>Electronics</topic><topic>Errors</topic><topic>Exact sciences and technology</topic><topic>Feedforward systems</topic><topic>Insulation life</topic><topic>Insulator testing</topic><topic>Insulators</topic><topic>Leakage current</topic><topic>neural network</topic><topic>Neural networks</topic><topic>Plastic insulation</topic><topic>polymeric insulator</topic><topic>Polymers</topic><topic>Rubber</topic><topic>salt-fog test</topic><topic>Silicone rubber</topic><topic>Training</topic><topic>Various equipment and components</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jahromi, A.N.</creatorcontrib><creatorcontrib>El-Hag, A.H.</creatorcontrib><creatorcontrib>Jayaram, S.H.</creatorcontrib><creatorcontrib>Cherney, E.A.</creatorcontrib><creatorcontrib>Sanaye-Pasand, M.</creatorcontrib><creatorcontrib>Mohseni, H.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE transactions on power delivery</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jahromi, A.N.</au><au>El-Hag, A.H.</au><au>Jayaram, S.H.</au><au>Cherney, E.A.</au><au>Sanaye-Pasand, M.</au><au>Mohseni, H.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A neural network based method for leakage current prediction of polymeric insulators</atitle><jtitle>IEEE transactions on power delivery</jtitle><stitle>TPWRD</stitle><date>2006-01</date><risdate>2006</risdate><volume>21</volume><issue>1</issue><spage>506</spage><epage>507</epage><pages>506-507</pages><issn>0885-8977</issn><eissn>1937-4208</eissn><coden>ITPDE5</coden><abstract>This letter describes a neural network approach to the prediction of the leakage current (LC) on silicone rubber insulators exposed to salt-fog. 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| identifier | ISSN: 0885-8977 |
| ispartof | IEEE transactions on power delivery, 2006-01, Vol.21 (1), p.506-507 |
| issn | 0885-8977 1937-4208 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Aging Applied sciences Back propagation Chambers Degradation Electric, optical and optoelectronic circuits Electrical engineering. Electrical power engineering Electronics Errors Exact sciences and technology Feedforward systems Insulation life Insulator testing Insulators Leakage current neural network Neural networks Plastic insulation polymeric insulator Polymers Rubber salt-fog test Silicone rubber Training Various equipment and components |
| title | A neural network based method for leakage current prediction of polymeric insulators |
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