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FPGA implementation of neural network classifier for partial discharge time resolved data from magnetic probe

Partial discharge (PD) is a common reason that causes electrical breakdown in high voltage underground XLPE cables. This paper proposes a concept of how to build an on-line, on-site system that is able to diagnose the severity of PD activities in XLPE cable as well as differentiate different types o...

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Main Authors:	Nguyen T.N.T., Chandan K.C., Ahmad B.A.G., Yap K.S.
Other Authors:	54584502600
Format:	Conference paper
Published:	2023
Subjects:	FPGA magnetic probes neural network partial discharge statistical method wavelet transform Field programmable gate arrays (FPGA) Low noise amplifiers Neural networks Personal computers Probes Statistical methods Underground cables Wavelet transforms Analog to digital converters Discharge time Electrical breakdown FPGA implementations High voltage Input signal Magnetic probes Neural network classifier Onsite systems Time-resolved Wave forms XLPE cables Partial discharges
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id	my.uniten.dspace-30424
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spelling	my.uniten.dspace-304242023-12-29T15:47:41Z FPGA implementation of neural network classifier for partial discharge time resolved data from magnetic probe Nguyen T.N.T. Chandan K.C. Ahmad B.A.G. Yap K.S. 54584502600 6701755282 56102167200 24448864400 FPGA magnetic probes neural network partial discharge statistical method wavelet transform Field programmable gate arrays (FPGA) Low noise amplifiers Neural networks Personal computers Probes Statistical methods Underground cables Wavelet transforms Analog to digital converters Discharge time Electrical breakdown FPGA implementations High voltage Input signal Magnetic probes Neural network classifier Onsite systems Time-resolved Wave forms XLPE cables Partial discharges Partial discharge (PD) is a common reason that causes electrical breakdown in high voltage underground XLPE cables. This paper proposes a concept of how to build an on-line, on-site system that is able to diagnose the severity of PD activities in XLPE cable as well as differentiate different types of PD signals. The system consists of magnetic probes, low noise amplifier, 3GSPS analog to digital converter (ADC) and a field programmable gate array (FPGA) board. The energy of PD signals is used to assess the severity of PD activities and artificial neural network (ANN) is used to classify different types of PD waveforms. In addition, wavelet transform is used to clean the time-resolved input signals and statistical method is used to extract important features of PD signals to fetch into neural network. The training of ANN is done on personal computer. The prototype and results of the research is elaborated in this paper. � 2011 IEEE. Final 2023-12-29T07:47:41Z 2023-12-29T07:47:41Z 2011 Conference paper 10.1109/APAP.2011.6180444 2-s2.0-84860689238 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84860689238&doi=10.1109%2fAPAP.2011.6180444&partnerID=40&md5=367d97167623d0975982902db8c5aafa https://irepository.uniten.edu.my/handle/123456789/30424 1 6180444 451 455 Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	FPGA magnetic probes neural network partial discharge statistical method wavelet transform Field programmable gate arrays (FPGA) Low noise amplifiers Neural networks Personal computers Probes Statistical methods Underground cables Wavelet transforms Analog to digital converters Discharge time Electrical breakdown FPGA implementations High voltage Input signal Magnetic probes Neural network classifier Onsite systems Time-resolved Wave forms XLPE cables Partial discharges
spellingShingle	FPGA magnetic probes neural network partial discharge statistical method wavelet transform Field programmable gate arrays (FPGA) Low noise amplifiers Neural networks Personal computers Probes Statistical methods Underground cables Wavelet transforms Analog to digital converters Discharge time Electrical breakdown FPGA implementations High voltage Input signal Magnetic probes Neural network classifier Onsite systems Time-resolved Wave forms XLPE cables Partial discharges Nguyen T.N.T. Chandan K.C. Ahmad B.A.G. Yap K.S. FPGA implementation of neural network classifier for partial discharge time resolved data from magnetic probe
description	Partial discharge (PD) is a common reason that causes electrical breakdown in high voltage underground XLPE cables. This paper proposes a concept of how to build an on-line, on-site system that is able to diagnose the severity of PD activities in XLPE cable as well as differentiate different types of PD signals. The system consists of magnetic probes, low noise amplifier, 3GSPS analog to digital converter (ADC) and a field programmable gate array (FPGA) board. The energy of PD signals is used to assess the severity of PD activities and artificial neural network (ANN) is used to classify different types of PD waveforms. In addition, wavelet transform is used to clean the time-resolved input signals and statistical method is used to extract important features of PD signals to fetch into neural network. The training of ANN is done on personal computer. The prototype and results of the research is elaborated in this paper. � 2011 IEEE.
author2	54584502600
author_facet	54584502600 Nguyen T.N.T. Chandan K.C. Ahmad B.A.G. Yap K.S.
format	Conference paper
author	Nguyen T.N.T. Chandan K.C. Ahmad B.A.G. Yap K.S.
author_sort	Nguyen T.N.T.
title	FPGA implementation of neural network classifier for partial discharge time resolved data from magnetic probe
title_short	FPGA implementation of neural network classifier for partial discharge time resolved data from magnetic probe
title_full	FPGA implementation of neural network classifier for partial discharge time resolved data from magnetic probe
title_fullStr	FPGA implementation of neural network classifier for partial discharge time resolved data from magnetic probe
title_full_unstemmed	FPGA implementation of neural network classifier for partial discharge time resolved data from magnetic probe
title_sort	fpga implementation of neural network classifier for partial discharge time resolved data from magnetic probe
publishDate	2023
_version_	1806423967186026496
score	13.222552

FPGA implementation of neural network classifier for partial discharge time resolved data from magnetic probe

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