{"id":371,"date":"2016-07-14T15:02:45","date_gmt":"2016-07-14T18:02:45","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=371"},"modified":"2016-07-14T15:02:45","modified_gmt":"2016-07-14T18:02:45","slug":"vol4-no2-art4","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol4-no2\/vol4-no2-art4\/","title":{"rendered":"Intelig\u00eancia Artificial Aplicada \u00c0 Prote\u00e7\u00e3o De Transformadores De Pot\u00eancia"},"content":{"rendered":"<p><strong>T\u00edtulo:<\/strong> Intelig\u00eancia Artificial Aplicada \u00c0 Prote\u00e7\u00e3o De Transformadores De Pot\u00eancia<\/p>\n<p><strong>Autores:<\/strong> Segatto, \u00canio C.; Coury, Denis V.<\/p>\n<p align=\"justify\"><strong>Resumo:<\/strong> Este trabalho apresenta um sistema eficiente de prote\u00e7\u00e3o diferencial para transformadores de pot\u00eancia, atrav\u00e9s da teoria de Redes Neurais Artificiais (RNAs). O m\u00e9todo proposto trata a classifica\u00e7\u00e3o do sistema de prote\u00e7\u00e3o como um problema de reconhecimento de padr\u00f5es e constitui um m\u00e9todo alternativo aos algoritmos convencionais. Muitos fatores, tais como a energiza\u00e7\u00e3o do transformador e a satura\u00e7\u00e3o dos TCs, podem causar uma opera\u00e7\u00e3o inadequada do rel\u00e9 de prote\u00e7\u00e3o. Um sistema de prote\u00e7\u00e3o alternativo foi desenvolvido, incluindo um m\u00f3dulo baseado em RNA em substitui\u00e7\u00e3o aos filtros harm\u00f4nicos, usados no algoritmo convencional. Abordagens baseadas na reconstru\u00e7\u00e3o dos sinais distorcidos causados pela satura\u00e7\u00e3o dos TCs s\u00e3o tamb\u00e9m propostas. Essas rotinas s\u00e3o adicionadas ao algoritmo final de prote\u00e7\u00e3o. Com a utiliza\u00e7\u00e3o de ferramentas de intelig\u00eancia artificial em um algoritmo completo de prote\u00e7\u00e3o de transformadores, uma solu\u00e7\u00e3o precisa, r\u00e1pida e eficiente foi obtida, se comparada aos m\u00e9todos convencionais.<\/p>\n<p><strong>Palavras-chave:<\/strong> Prote\u00e7\u00e3o Diferencial; Redes Neurais Artificiais; Transformadores de Pot\u00eancia; Satura\u00e7\u00e3o do TC<\/p>\n<p><strong>P\u00e1ginas:<\/strong> 9<\/p>\n<p><strong>C\u00f3digo DOI:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol4-no2-art4\">10.21528\/lmln-vol4-no2-art4<\/a><\/p>\n<p><strong>Artigo em PDF:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2016\/07\/vol4-no2-art4.pdf\" rel=\"\">vol4-no2-art4.pdf<\/a><\/p>\n<p><strong>Arquivo BibTex:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2016\/07\/vol4-no2-art4.bib\" rel=\"\">vol4-no2-art4.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>T\u00edtulo: Intelig\u00eancia Artificial Aplicada \u00c0 Prote\u00e7\u00e3o De Transformadores De Pot\u00eancia Autores: Segatto, \u00canio C.; Coury, Denis V. Resumo: Este trabalho apresenta um sistema eficiente de prote\u00e7\u00e3o diferencial para transformadores de pot\u00eancia, atrav\u00e9s da teoria de Redes Neurais Artificiais (RNAs). O <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol4-no2\/vol4-no2-art4\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":359,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-371","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Intelig\u00eancia Artificial Aplicada \u00c0 Prote\u00e7\u00e3o De Transformadores De Pot\u00eancia - Learning and NonLinear Models<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol4-no2\/vol4-no2-art4\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Intelig\u00eancia Artificial Aplicada \u00c0 Prote\u00e7\u00e3o De Transformadores De Pot\u00eancia - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"T\u00edtulo: Intelig\u00eancia Artificial Aplicada \u00c0 Prote\u00e7\u00e3o De Transformadores De Pot\u00eancia Autores: Segatto, \u00canio C.; Coury, Denis V. Resumo: Este trabalho apresenta um sistema eficiente de prote\u00e7\u00e3o diferencial para transformadores de pot\u00eancia, atrav\u00e9s da teoria de Redes Neurais Artificiais (RNAs). 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Resumo: Este trabalho apresenta um sistema eficiente de prote\u00e7\u00e3o diferencial para transformadores de pot\u00eancia, atrav\u00e9s da teoria de Redes Neurais Artificiais (RNAs). 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