Open AccessCase‐based power transformer diagnose model using nonlinear mapping of oil chromatography
Author(s) -
Zheng Yiming,
Yao Hui,
Sheng Jun,
He Wenlin,
Sun Xiang,
Wang Wenhao,
Zhan Jiangyang
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.8372
Subject(s) - transformer , nonlinear system , computer science , transformer oil , similarity (geometry) , algorithm , pattern recognition (psychology) , artificial intelligence , engineering , image (mathematics) , physics , quantum mechanics , voltage , electrical engineering
In this study, a case‐based diagnostic model of a power transformer for oil chromatography is proposed. A nonlinear normalised mapping method is proposed for achieving ideal distribution characteristics of oil chromatogram gas in the case base. A similarity degree algorithm is proposed to calculate the similarity degree in the target spatial domain between the case to be diagnosed and the cases in the case base. A threshold method is proposed to determine the number of similar cases. Also, a method for calculating the weight of the diagnosis result according to the case distribution in the case base is proposed. The diagnostic method proposed is used to deploy the diagnostic system on the Internet. Defeat diagnosis conclusions of two transformers by the method and system in this paper are consistent with the disintegration results. It can be concluded that the proposed method has important practical application value in engineering.