Please use this identifier to cite or link to this item:
|Title:||Real-time detection using wavelet transform and neural network of short-circuit faults within a train in DC transit systems||Authors:||Chang, C.S.
|Issue Date:||May-2001||Citation:||Chang, 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||Abstract:||A 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.||Source Title:||IEE Proceedings: Electric Power Applications||URI:||http://scholarbank.nus.edu.sg/handle/10635/51023||ISSN:||13502352||DOI:||10.1049/ip-epa:20010350|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.