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 third-rail 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.


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    Title :

    Real-time detection using wavelet transform and neural network of short-circuit faults within a train in DC transit systems


    Contributors:
    Chang, C.S. (author) / Kumar, S. (author) / Liu, B. (author) / Khambadkone, A. (author)

    Published in:

    Publication date :

    2001


    Size :

    6 Seiten, 13 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English





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