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dc.contributor.authorChoong, F
dc.contributor.authorReaz, MBI
dc.contributor.authorMohd-Yasin, F
dc.date.accessioned2017-05-03T11:49:47Z
dc.date.available2017-05-03T11:49:47Z
dc.date.issued2005
dc.date.modified2012-09-04T23:01:12Z
dc.identifier.isbn9780769523125
dc.identifier.doi10.1109/IPDPS.2005.348
dc.identifier.urihttp://hdl.handle.net/10072/46702
dc.description.abstractIdentification and classification of voltage and current disturbances in power systems is an important task in power system monitoring and protection. Most power quality disturbances are non-stationary and transitory and the detection and classification have proved to be very demanding. New intelligent system technologies using wavelet transform, expert systems and artificial neural networks provide some unique advantages regarding fault analysis. This paper presents new approach aimed at automating the analysis of power quality disturbances including sag, swell, transient, fluctuation, interruption and normal waveform. The approach focuses on the application of discrete wavelet transform technique to extract features from disturbance waveforms and their classification using a powerful combination of neural network and fuzzy logic. The system is modelled using VHDL followed by extensive testing and simulation to verify the correct functionality of the system. Then, the design is synthesized to APEX EP20K200EBC652-1X FPGA, tested and validated. Comparisons, verification and analysis made from the results obtained from the application of this system on software-generated and utility sampled disturbance signals validate the utility of this approach and achieved a classification accuracy of 98.17%.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent138838 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.publisherIEEE
dc.publisher.placeUnited States
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencename19th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2005)
dc.relation.ispartofconferencetitleProceedings - 19th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2005
dc.relation.ispartofdatefrom2005-04-04
dc.relation.ispartofdateto2005-04-08
dc.relation.ispartoflocationDenver, Colorado
dc.relation.ispartofvolume2005
dc.rights.retentionY
dc.subject.fieldofresearchElectrical and Electronic Engineering not elsewhere classified
dc.subject.fieldofresearchcode090699
dc.titlePower Quality Disturbance Detection Using Artificial Intelligence: A Hardware Approach
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.rights.copyright© 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
gro.date.issued2005
gro.hasfulltextFull Text
gro.griffith.authorMohd-Yasin, Faisal


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  • Conference outputs
    Contains papers delivered by Griffith authors at national and international conferences.

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