Application of Probabilistic Neural Networks Using High-Frequency Components’ Differential Current for Transformer Protection Schemes to Discriminate between External Faults and Internal Winding Faults in Power Transformers

Application of Probabilistic Neural Networks Using High-Frequency Components’ Differential Current for Transformer Protection Schemes to Discriminate between External Faults and Internal Winding Faults in Power Transformers

Internal and external faults in a power transformer are discriminated in this paper using an algorithm based on a combination of a discrete wavelet transform (DWT) and a probabilistic neural network (PNN). DWT decomposes high-frequency fault components using the maximum coefficients of a ¼ cycle DWT...

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Auteurs principaux: Pathomthat Chiradeja, Chaichan Pothisarn, Nattanon Phannil, Santipont Ananwattananporn, Monthon Leelajindakrairerk, Atthapol Ngaopitakkul, Surakit Thongsuk, Vinai Pornpojratanakul, Sulee Bunjongjit, Suntiti Yoomak
Format: article
Langue:EN
Publié: MDPI AG 2021
Sujets:
difference relay
fault detection
power transformer
wavelet transform (WT)
probabilistic neural network (PNN)
Technology
T
Engineering (General). Civil engineering (General)
TA1-2040
Biology (General)
QH301-705.5
Physics
QC1-999
Chemistry
QD1-999
Accès en ligne:https://doaj.org/article/a4e3e09087534ae796ca94864acbd9e0
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