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    Ann-Based Fault Classification And Location On Mvdc Cables Of Shipboard Power Systems

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    etd-11022011-230909.pdf (734.3 Kb )
    Author
    Chanda, Naveen Kumar
    Item Type
    Thesis
    Committee
    Grzybowski, Stanislaw
    Abdelwahed, Sherif
    Fu, Yong
    Metrics
    
    Abstract
    Uninterrupted power supply is an important requirement for electric ship since it has to confront frequent travel and hostilities. However, the occurrence of faults in the shipboard power systems interrupts the power service continuity and leads to the severe damage on the electrical equipments. Faults need to be quickly detected and isolated in order to restore the power supply and prevent the massive cascading outage effect on the electrical equipments. This thesis presents an Artificial Neural Network (ANN) based method for the fault classification and location in MVDC shipboard power systems using the transient information in the fault voltage and current waveforms. The proposed approach is applied to the cable of an equivalent MVDC system which is simulated using PSCAD. The proposed method is efficient in detecting the type and location of DC cable faults and is not influenced by changes in electrical parameters like fault resistance and load.
    Degree
    Master of Science
    Major
    Electrical Engineering
    Department
    Department of Electrical and Computer Engineering.
    URI
    https://hdl.handle.net/11668/17040
    Collections
    • Theses and Dissertations
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    Mississippi State University Libraries
    395 Hardy Rd
    P.O. Box 5408, Mississippi State, MS 39762-5408
    (662) 325-7668
    (662) 325-0011
    (662) 325-8183
    Contact repository admin Report a problem Terms of use Privacy policy Accessibility MSU Legal
     

     

    Mississippi State University Libraries
    395 Hardy Rd
    P.O. Box 5408, Mississippi State, MS 39762-5408
    (662) 325-7668
    (662) 325-0011
    (662) 325-8183
    Contact repository admin Report a problem Terms of use Privacy policy Accessibility MSU Legal