Title :
A hybrid tool for detection of incipient faults in transformers based on the dissolved gas analysis of insulating oil
Author :
Morais, Diego ; Rolim, Jacqueline
Author_Institution :
Santa Catarina Federal Univ.
Abstract :
Summary form only given. This paper describes the development and the implementation of a tool for the diagnosis of faults in power transformers through the analysis of dissolved gases in oil. The computational system approach is based on a combined use of some traditional criteria of the dissolved gas analysis published in standards, an artificial neural network and a fuzzy logic system. The objective of the tool is to provide the user with an answer obtained from analysis not only of the traditional methods already consolidated in the technical literature, but also via artificial intelligence techniques, reaching a higher degree of reliability with respect to each technique individually. The results obtained with this tool are promising in the diagnosis of incipient faults in transformers, reaching success levels above 80%
Keywords :
fault location; fuzzy logic; neural nets; power engineering computing; power transformer insulation; power transformer protection; transformer oil; artificial neural network; dissolved gas analysis; fuzzy logic system; incipient faults detection; insulating oil; transformer incipient faults; Computer networks; Dissolved gas analysis; Fault detection; Fault diagnosis; Gas insulation; Gases; Oil insulation; Petroleum; Power transformer insulation; Power transformers;
Conference_Titel :
Power Engineering Society General Meeting, 2006. IEEE
Conference_Location :
Montreal, Que.
Print_ISBN :
1-4244-0493-2
DOI :
10.1109/PES.2006.1709217
