Transformer failure diagnosis by means of fuzzy rules extracted from Kohonen Self-Organizing Map

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

Autores

Ana Carla Macedo da Silva (Autor) Outra; Adriana Garcez Castro (Autor) FEUP; Vladimiro Miranda (Autor) FEUP

Revista

Título: International Journal of Electrical Power and Energy Systems

Vol. 43

Páginas: 1034-1042

ISSN: 0142-0615

Editora: Elsevier

Indexação

ISI Web of Knowledge - 11 Citações

ISI Web of Science

Scopus - 20 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-002-2XH

DOI: 10.1016/j.ijepes.2012.06.027

Abstract (EN): This paper presents a transformer failure diagnosis system based on Dissolved Gases Analysis that was developed by using a new methodology for extracting fuzzy rules from Kohonen Self-Organizing Map. Firstly, the Kohonen net was trained in order to capture the knowledge from a database of faulty transformers inspected in service. Once the knowledge was captured during the learning stage, it was transformed into the form of Zero-order Takagi-Sugeno fuzzy rules. In the form of fuzzy rules, the relationship between the variables of the system became explicit which have led to a more reliable diagnosis system. Additionally to the extraction of the fuzzy system, a fuzzyfication process was applied in the fuzzy system output. Experimental results demonstrated the efficiency of the diagnosis system proposed that had superior results as compared with other conventional and intelligent methods.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9