Please use this identifier to cite or link to this item: https://doi.org/10.1049/ip-epa:20010350
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. 
Kumar, S. 
Liu, B. 
Khambadkone, A. 
Issue Date: May-2001
Source: 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
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