Please use this identifier to cite or link to this item: https://doi.org/10.1049/ip-epa:20010350
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dc.titleReal-time detection using wavelet transform and neural network of short-circuit faults within a train in DC transit systems
dc.contributor.authorChang, C.S.
dc.contributor.authorKumar, S.
dc.contributor.authorLiu, B.
dc.contributor.authorKhambadkone, A.
dc.date.accessioned2014-04-24T07:24:18Z
dc.date.available2014-04-24T07:24:18Z
dc.date.issued2001-05
dc.identifier.citationChang, C.S., Kumar, S., Liu, B., Khambadkone, A. (2001-05). Real-time detection using wavelet transform and neural network of short-circuit faults within a train in DC transit systems. IEE Proceedings: Electric Power Applications 148 (3) : 251-256. ScholarBank@NUS Repository. https://doi.org/10.1049/ip-epa:20010350
dc.identifier.issn13502352
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/51023
dc.description.abstractA method is proposed for the real-time detection of DC-link short-circuit faults in DC transit systems. The discrete wavelet transform is implemented to detect any surges in the DC thirdrail current waveform. In the event of a surge the wavelet transform extracts a feature vector from the current waveform and feeds it to a self-organising neural network. The neural network determines whether the feature vector belongs to a normal or a fault current surge.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1049/ip-epa:20010350
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentDATA STORAGE INSTITUTE
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1049/ip-epa:20010350
dc.description.sourcetitleIEE Proceedings: Electric Power Applications
dc.description.volume148
dc.description.issue3
dc.description.page251-256
dc.description.codenIEPAE
dc.identifier.isiut000169192500004
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