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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Detalles Bibliográficos
Autores principales:	Pathomthat Chiradeja, Chaichan Pothisarn, Nattanon Phannil, Santipont Ananwattananporn, Monthon Leelajindakrairerk, Atthapol Ngaopitakkul, Surakit Thongsuk, Vinai Pornpojratanakul, Sulee Bunjongjit, Suntiti Yoomak
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	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
Acceso en línea:	https://doaj.org/article/a4e3e09087534ae796ca94864acbd9e0
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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

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