Transformer fault diagnosis based on autoassociative neural networks

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2011

Autores

Castro, ARG (Autor) Outra; Vladimiro Miranda (Autor) FEUP; Lima, S (Autor) Outra

Ata de Conferência Internacional

Título: 2011 16th International Conference on Intelligent System Applications to Power Systems, ISAP 2011

2011 16th International Conference on Intelligent System Applications to Power Systems, ISAP 2011

Hersonisos, Crete, 25 September 2011 through 28 September 2011

Indexação

Scopus - 8 Citações

Outras Informações

ID Authenticus: P-008-1HW

DOI: 10.1109/isap.2011.6082196

Abstract (EN): This paper presents a new approach to incipient fault diagnosis in power transformers, based on the results of dissolved gas analysis. A set of autoassociative neural networks or autoencoders are trained, so that each becomes tuned with a particular fault mode. Then, a parallel model is built where the autoencoders compete with one another when a new input vector is entered and the closest recognition is taken as the diagnosis sought. A remarkable accuracy is achieved with this architecture, in a large data set used for result validation. © 2011 IEEE.

Idioma: Inglês

Tipo (Avaliação Docente): Científica