Diagnóstico de falhas incipientes a partir das propriedades físico-químicas do óleo isolante em transformadores de potência como método alternativo à análise de gases dissolvidos

• Ano de defesa: 2013
• Autor(a) principal: Barbosa, Fábio Rocha
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Palavras-chave em Português: Engenharia elétrica; Transformadores elétricos; Redes neurais
• Link de acesso: http://www.repositorio.ufc.br/handle/riufc/4661

The diagnosis of incipient fault in power transformers immerses in oil are directly related to the assessment of the isolation system conditions. This search is about the relationship between dissolved gases and the quality of the insulating mineral oil used in power transformers. Artificial Neural Networks are used to approach operational conditions assessment issue of the insulating oil in power transformers, which is characterized by a nonlinear dynamic behavior. The operation conditions and integrity of a power transformer can be inferred by analysis of physicochemical and chromatographic (DGA – Dissolved Gas Analysis) profiles of the isolating oil. This tests allow establishing procedures for operating and maintaining the equipment and usually are performed simultaneously. This work proposes a method that can be used to extract chromatographic information using physicochemical analysis through Artificial Neural Networks. The present analysis of physicochemical properties only provide a diagnostic tool for the oil quality, which does not allow the diagnosis of incipient faults. It´s believed that, the power utilities could improve reliability in the prediction of incipient failures at a lower cost with this method, since only one test is required. The results show this strategy might be promising with an average accuracy for diagnosis of faults greater than 72%. The purpose of this work is the direct implementation of the diagnosis of incipient faults through the use of physicochemical properties without the need to make an oil chromatography.