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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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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spelling	oai:doaj.org-article:a4e3e09087534ae796ca94864acbd9e02021-11-25T16:33:26ZApplication 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 Transformers10.3390/app1122106192076-3417https://doaj.org/article/a4e3e09087534ae796ca94864acbd9e02021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10619https://doaj.org/toc/2076-3417Internal 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 as input patterns for the training process in a decision algorithm. A division algorithm between a zero sequence of post-fault differential current waveforms and the differential current coefficient in the ¼ cycle DWT is used to detect the maximum ratio and faults. The simulation system uses various study cases based on Thailand’s electricity transmission and distribution systems. The simulation results demonstrated that the PNN and BPNN are effectively implemented and perform fault detection with satisfactory accuracy. However, the PNN method is most suitable for detecting internal and external faults, and the maximum coefficient algorithm is the most effective in detecting the fault. This study will be useful in differential protection for power transformers.Pathomthat ChiradejaChaichan PothisarnNattanon PhannilSantipont AnanwattananpornMonthon LeelajindakrairerkAtthapol NgaopitakkulSurakit ThongsukVinai PornpojratanakulSulee BunjongjitSuntiti YoomakMDPI AGarticledifference relayfault detectionpower transformerwavelet transform (WT)probabilistic neural network (PNN)TechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10619, p 10619 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	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
spellingShingle	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 Pathomthat Chiradeja Chaichan Pothisarn Nattanon Phannil Santipont Ananwattananporn Monthon Leelajindakrairerk Atthapol Ngaopitakkul Surakit Thongsuk Vinai Pornpojratanakul Sulee Bunjongjit Suntiti Yoomak 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
description	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 as input patterns for the training process in a decision algorithm. A division algorithm between a zero sequence of post-fault differential current waveforms and the differential current coefficient in the ¼ cycle DWT is used to detect the maximum ratio and faults. The simulation system uses various study cases based on Thailand’s electricity transmission and distribution systems. The simulation results demonstrated that the PNN and BPNN are effectively implemented and perform fault detection with satisfactory accuracy. However, the PNN method is most suitable for detecting internal and external faults, and the maximum coefficient algorithm is the most effective in detecting the fault. This study will be useful in differential protection for power transformers.
format	article
author	Pathomthat Chiradeja Chaichan Pothisarn Nattanon Phannil Santipont Ananwattananporn Monthon Leelajindakrairerk Atthapol Ngaopitakkul Surakit Thongsuk Vinai Pornpojratanakul Sulee Bunjongjit Suntiti Yoomak
author_facet	Pathomthat Chiradeja Chaichan Pothisarn Nattanon Phannil Santipont Ananwattananporn Monthon Leelajindakrairerk Atthapol Ngaopitakkul Surakit Thongsuk Vinai Pornpojratanakul Sulee Bunjongjit Suntiti Yoomak
author_sort	Pathomthat Chiradeja
title	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
title_short	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
title_full	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
title_fullStr	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
title_full_unstemmed	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
title_sort	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
publisher	MDPI AG
publishDate	2021
url	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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