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    A Machine Learning Based Tool for Voltage Dip Classification

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    CIRED 2019 - 985.pdf (523.0Kb)
    Paper number
    985
    Conference name
    CIRED 2019
    Conference date
    3-6 June 2019
    Conference location
    Madrid, Spain
    Peer-reviewed
    Yes
    Metadata
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    Authors
    Shadmehr, Houriyeh, Ricerca sul Sistema Energetico RSE, Italy
    Chiumeo, Riccardo, RSE spa, Italy
    Tenti, Liliana, Ricerca sul Sistema Energetico RSE, Italy
    Abstract
    A Machine Learning based tool is presented in order to make voltage dips (VD) ex-post analysis more automatic and effortless. The tool takes as input the full waveforms associated to voltage dips occurring in the Italian MV networks and recorded by QuEEN monitoring system implemented by RSE. The first tool has been developed to classify events on the base of their HV/MV origin since the utilities will be responsible only for the events due to faults occurred in their networks; it uses the self-tuning Kalman Filter and Support Vector Machine (SVM) for extracting the VD’s features and classifying the events, respectively.Instead, the second tool, based on end-to-end Deep Learning techniques, has been developed to distinguish between “true” and “false” VD; it utilizes a Convolutional Neural Network (CNN) whose first layers undertake the task of the features extraction while the last layers carry out the events classification.
    Publisher
    AIM
    Date
    2019-06-03
    Published in
    • CIRED 2019 Conference
    Permanent link to this record
    https://cired-repository.org/handle/20.500.12455/235
    http://dx.doi.org/10.34890/456
    ISSN
    2032-9644
    ISBN
    978-2-9602415-0-1

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