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

• Type: Article in International Scientific Journal
• Date: 2012

Authors

Ana Carla Macedo da Silva (Author) Other; Adriana Garcez Castro (Author) FEUP; Vladimiro Miranda (Author) FEUP

Journal

Title: International Journal of Electrical Power and Energy Systems

Vol. 43

Pages: 1034-1042

ISSN: 0142-0615

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 11 Citations

ISI Web of Science

Scopus - 20 Citations

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

CORDIS: Technological sciences > Engineering > Electrical engineering

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9