Diagnosing Faults in Power Transformers With Autoassociative Neural Networks and Mean Shift

• Tipo: Artigo em Revista Científica Internacional
• Data: 2012

Autores

Vladimiro Miranda (Autor) FEUP; Adriana Castro (Autor) FEUP; Shigeaki Lima (Autor) FEUP

Revista

Título: IEEE Transactions on Power Delivery

Vol. 27 Nº 3

Páginas: 1350-1357

ISSN: 0885-8977

Editora: IEEE

Indexação

ISI Web of Knowledge - 48 Citações

ISI Web of Science

Scopus - 81 Citações

Classificação Científica

FOS: Ciências da engenharia e tecnologias > Engenharia electrotécnica, electrónica e informática

CORDIS: Ciências Tecnológicas > Engenharia > Engenharia electrotécnica

Outras Informações

ID Authenticus: P-002-8F7

DOI: 10.1109/tpwrd.2012.2188143

Resumo (PT): 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 or no fault condition. The scarce data available forms clusters that are densified using an Information Theoretic Mean Shift algorithm, so that training becomes efficient. 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 of 100% is achieved with this architecture, in a validation data set using all real information available.

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 is trained, so that each becomes tuned with a particular fault mode or no fault condition. The scarce data available forms clusters that are densified using an Information Theoretic Mean Shift algorithm, allowing all real data to be used in the validation process. 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 of 100% is achieved with this architecture, in a validation data set using all real information available.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8