• Admin Login
    View Item 
    •   CIRED Repository Home
    • CIRED Proceedings
    • CIRED 2019 Conference
    • View Item
    •   CIRED Repository Home
    • CIRED Proceedings
    • CIRED 2019 Conference
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Empirical end-device disturbance recognition by waveform feature learning models

    Thumbnail
    View/Open
    CIRED 2019 - 119.pdf (599.8Kb)
    Paper number
    119
    Conference name
    CIRED 2019
    Conference date
    3-6 June 2019
    Conference location
    Madrid, Spain
    Peer-reviewed
    Yes
    Metadata
    Show full item record
    Authors
    Moon, Sang-keun, Korea Electric Power Cooperation (KEPCO), Korea Republic of
    Joung, Jong-man, Korea Electric Power Cooperation (KEPCO), Korea Republic of
    Lee, Byungsung, KEPCO Research Institute, Korea Republic of
    Kim, Jin-o, Hanyang University, Korea Republic of
    Abstract
    A waveform holds recognizable feature patterns. To extract such features of various equipment disturbance conditions from the waveform, we present a practical model to estimate distribution line (DL) conditions by means of a multi-label extreme learning machine. The motivation for the waveform learning is to develop device embedded models which are capable of detecting and classifying abnormal operations on the DLs. The model with the real DL waveform data holds a potential for determining additional DL conditions and improving its classification performance through the update mechanism of the learning machine. On the other hand, conditional structures for distribution networks are discovered with respect to distribution network configurations and measurement device characteristics by the time-scaled class map of obtained measurement data from the field.
    Publisher
    AIM
    Date
    2019-06-03
    Published in
    • CIRED 2019 Conference
    Permanent link to this record
    https://cired-repository.org/handle/20.500.12455/426
    http://dx.doi.org/10.34890/651
    ISSN
    2032-9644
    ISBN
    978-2-9602415-0-1

    Browse

    All of CIRED RepositoryCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    Admin LoginRegister

    DSpace software copyright © 2002-2023  DuraSpace
    Contact Us | Send Feedback
    DSpace Express is a service operated by 
    Atmire NV