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. |
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Aug 18, 2018
WEB OF SCIENCETM
checked on Jul 11, 2018
checked on Jun 22, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.