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

dc.contributor.author Vladimiro Miranda en
dc.contributor.author Ana Carla Macedo da Silva en
dc.contributor.author Adriana Garcez Castro en
dc.date.accessioned 2017-11-16T13:42:55Z
dc.date.available 2017-11-16T13:42:55Z
dc.date.issued 2012 en
dc.description.abstract 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. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/2471
dc.language eng en
dc.relation 208 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Transformer failure diagnosis by means of fuzzy rules extracted from Kohonen Self-Organizing Map en
dc.type article en
dc.type Publication en
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