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Recognition of partial discharge pattern of electrode voids by neural network
Author(s) -
Yanagisawa Takashi,
Iwamoto Shinichi,
Okamoto Tatsuki,
Fukagawa Hiromasa
Publication year - 1992
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.4391120212
Subject(s) - electrode , partial discharge , materials science , artificial neural network , phase (matter) , tree (set theory) , biological system , computer science , electrical engineering , artificial intelligence , voltage , engineering , mathematics , chemistry , mathematical analysis , biology , organic chemistry
A back‐propagation neural network model is used to identify electrode types involved in partial discharge (tree, IEC (b), CIGRE method I) and to estimate the shapes of the cylindrical electrode voids, based on the specific charge‐phase characteristics of the different electrode types.