Diagnosis of oil-insulated power apparatus by using neural network simulation

Autor:	Katsuhiko Naito, T. Kamiya, O. Vanegas, Yukio Mizuno
Rok vydání:	1997
Předmět:	Engineering Relation (database) Artificial neural network business.industry Transformer oil Electrical engineering Process (computing) Control engineering Power apparatus High voltage Neural Network Simulation Pattern recognition (psychology) Electrical and Electronic Engineering business
Zdroj:	IEEE Transactions on Dielectrics and Electrical Insulation. 4:290-299
ISSN:	1070-9878
Popis:	One diagnostic process for oil-insulated power apparatus is based on the analysis of the chemical composition of gases evolved by the insulating oil. Normally this can be done only by a human expert. A considerable amount of information on the relation between chemical components and the faulty part of the power apparatus has been accumulated. This paper describes a neural network system which can be applied to existing diagnostic methods to enable analysis even by inexperienced engineers.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::5f4cd72941b8e704a61a670656335f85 https://doi.org/10.1109/94.598285 Zobrazit plný text záznamu