Faults location in transmission lines through neural networks
This work studies the viability of the application of computational techniques, more specifically the artificial neural networks (ANN), in the identification and location of faults in transmission lines using voltages and currents signals registered by numeric relays in one of the terminals of a tra...
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creator | Amorim, H.P. Huais, L. |
description | This work studies the viability of the application of computational techniques, more specifically the artificial neural networks (ANN), in the identification and location of faults in transmission lines using voltages and currents signals registered by numeric relays in one of the terminals of a transmission line. One model of ANN was used in the fault identification and other four were used on the fault location, being one for each type: single-phase, two-phase, two-phase-earth and three-phase. |
doi_str_mv | 10.1109/TDC.2004.1432463 |
format | Conference Proceeding |
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One model of ANN was used in the fault identification and other four were used on the fault location, being one for each type: single-phase, two-phase, two-phase-earth and three-phase.</description><identifier>ISBN: 9780780387751</identifier><identifier>ISBN: 0780387759</identifier><identifier>DOI: 10.1109/TDC.2004.1432463</identifier><language>eng</language><publisher>IEEE</publisher><subject>Artificial neural networks ; Computer applications ; Computer networks ; Fault diagnosis ; Fault location ; Neural networks ; Relays ; Signal processing ; Transmission lines ; Voltage</subject><ispartof>2004 IEEE/PES Transmision and Distribution Conference and Exposition: Latin America (IEEE Cat. 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No. 04EX956)</title><addtitle>TDC</addtitle><description>This work studies the viability of the application of computational techniques, more specifically the artificial neural networks (ANN), in the identification and location of faults in transmission lines using voltages and currents signals registered by numeric relays in one of the terminals of a transmission line. One model of ANN was used in the fault identification and other four were used on the fault location, being one for each type: single-phase, two-phase, two-phase-earth and three-phase.</description><subject>Artificial neural networks</subject><subject>Computer applications</subject><subject>Computer networks</subject><subject>Fault diagnosis</subject><subject>Fault location</subject><subject>Neural networks</subject><subject>Relays</subject><subject>Signal processing</subject><subject>Transmission lines</subject><subject>Voltage</subject><isbn>9780780387751</isbn><isbn>0780387759</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2004</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj0FLxDAUhAMirKy9L3jpH2h9yUvS5uBBqqvCgpf1vKTpqxvttpKkiP_eijsMfAwDA8PYhkPJOZjb_UNTCgBZcolCarxgmalqWIx1VSm-YlmMH7AIja4Artjd1s5DivkwOZv8NOZ-zFOwYzz5GP_y4EeKeTqGaX4_5iPNwQ4L0vcUPuM1u-ztECk7c83eto_75rnYvT69NPe7wvNKpUK0LRmhOldT74iUckIjKNlhxwU6IzRxvjRKa9Mjl60ECUCdRuvIuRbX7OZ_1xPR4Sv4kw0_h_NJ_AXlXkhO</recordid><startdate>2004</startdate><enddate>2004</enddate><creator>Amorim, H.P.</creator><creator>Huais, L.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2004</creationdate><title>Faults location in transmission lines through neural networks</title><author>Amorim, H.P. ; Huais, L.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-2bbe925dc8efcee55c263054d3d123c926e11fce5669f314b40400ed63aceccb3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2004</creationdate><topic>Artificial neural networks</topic><topic>Computer applications</topic><topic>Computer networks</topic><topic>Fault diagnosis</topic><topic>Fault location</topic><topic>Neural networks</topic><topic>Relays</topic><topic>Signal processing</topic><topic>Transmission lines</topic><topic>Voltage</topic><toplevel>online_resources</toplevel><creatorcontrib>Amorim, H.P.</creatorcontrib><creatorcontrib>Huais, L.</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>Amorim, H.P.</au><au>Huais, L.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Faults location in transmission lines through neural networks</atitle><btitle>2004 IEEE/PES Transmision and Distribution Conference and Exposition: Latin America (IEEE Cat. No. 04EX956)</btitle><stitle>TDC</stitle><date>2004</date><risdate>2004</risdate><spage>691</spage><epage>695</epage><pages>691-695</pages><isbn>9780780387751</isbn><isbn>0780387759</isbn><abstract>This work studies the viability of the application of computational techniques, more specifically the artificial neural networks (ANN), in the identification and location of faults in transmission lines using voltages and currents signals registered by numeric relays in one of the terminals of a transmission line. One model of ANN was used in the fault identification and other four were used on the fault location, being one for each type: single-phase, two-phase, two-phase-earth and three-phase.</abstract><pub>IEEE</pub><doi>10.1109/TDC.2004.1432463</doi><tpages>5</tpages></addata></record> |
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language | eng |
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source | IEEE Electronic Library (IEL) Conference Proceedings |
subjects | Artificial neural networks Computer applications Computer networks Fault diagnosis Fault location Neural networks Relays Signal processing Transmission lines Voltage |
title | Faults location in transmission lines through neural networks |
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