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

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dc.citedby3
dc.contributor.authorNguyen T.N.T.en_US
dc.contributor.authorChandan K.C.en_US
dc.contributor.authorAhmad B.A.G.en_US
dc.contributor.authorYap K.S.en_US
dc.contributor.authorid54584502600en_US
dc.contributor.authorid6701755282en_US
dc.contributor.authorid56102167200en_US
dc.contributor.authorid24448864400en_US
dc.date.accessioned2023-12-29T07:47:41Z
dc.date.available2023-12-29T07:47:41Z
dc.date.issued2011
dc.description.abstractPartial 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo6180444
dc.identifier.doi10.1109/APAP.2011.6180444
dc.identifier.epage455
dc.identifier.scopus2-s2.0-84860689238
dc.identifier.spage451
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84860689238&doi=10.1109%2fAPAP.2011.6180444&partnerID=40&md5=367d97167623d0975982902db8c5aafa
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/30424
dc.identifier.volume1
dc.pagecount4
dc.sourceScopus
dc.sourcetitleAPAP 2011 - Proceedings: 2011 International Conference on Advanced Power System Automation and Protection
dc.subjectFPGA
dc.subjectmagnetic probes
dc.subjectneural network
dc.subjectpartial discharge
dc.subjectstatistical method
dc.subjectwavelet transform
dc.subjectField programmable gate arrays (FPGA)
dc.subjectLow noise amplifiers
dc.subjectNeural networks
dc.subjectPersonal computers
dc.subjectProbes
dc.subjectStatistical methods
dc.subjectUnderground cables
dc.subjectWavelet transforms
dc.subjectAnalog to digital converters
dc.subjectDischarge time
dc.subjectElectrical breakdown
dc.subjectFPGA implementations
dc.subjectHigh voltage
dc.subjectInput signal
dc.subjectMagnetic probes
dc.subjectNeural network classifier
dc.subjectOnsite systems
dc.subjectTime-resolved
dc.subjectWave forms
dc.subjectXLPE cables
dc.subjectPartial discharges
dc.titleFPGA implementation of neural network classifier for partial discharge time resolved data from magnetic probeen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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