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    Object Detection Based Power Quality Expert System for an Electric Vehicle Infrastructure

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    CIRED 2019 - 958.pdf (538.5Kb)
    Paper number
    958
    Conference name
    CIRED 2019
    Conference date
    3-6 June 2019
    Conference location
    Madrid, Spain
    Peer-reviewed
    Yes
    Metadata
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    Authors
    Streubel, Tim , University of Stuttgart- Institute of Power Transmission and High Voltage Technology, Germany
    Eisenmann, Adrian , University of Stuttgart Institute of Power Transmission and High Voltage Technology, Germany
    Abstract
    The shift in power systems from large power stations towards smaller decentralized generation units imposes new challenges on grid operators regarding power quality. The steady increase of power electronics connected to the grid results in elevated harmonic distortion levels, inducing additional losses and causing malfunctioning of control devices. This development has led to a rising number of monitoring systems, in order to ensure adequate power quality within the mandatory limits and boundaries. Consequently, detection and classification of power quality disturbances is a vital component of the mentioned systems. This paper proposes a new method for detecting power quality issues and identifying their underlying causes, based on historical data. The implemented automated image classification algorithm continuously analyses the FFT spectrogram of disturbances and simultaneously determines the type of disturbance and its cause without the need of a segmentation and feature extraction process. The results are validated using measurement from an electric vehicle car charging station.
    Publisher
    AIM
    Date
    2019-06-03
    Published in
    • CIRED 2019 Conference
    Permanent link to this record
    https://cired-repository.org/handle/20.500.12455/218
    http://dx.doi.org/10.34890/427
    ISSN
    2032-9644
    ISBN
    978-2-9602415-0-1

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