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    Incipient Fault Prediction in Power Quality Monitoring

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    CIRED 2019 - 1535.pdf (311.4Kb)
    Paper number
    1535
    Conference name
    CIRED 2019
    Conference date
    3-6 June 2019
    Conference location
    Madrid, Spain
    Peer-reviewed
    Yes
    Metadata
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    Authors
    Hoffmann, Volker, SINTEF AS, Norway
    Michałowska, Katarzyna, SINTEF AS, Norway
    Andresen, Christian, SINTEF Energy Research AS, Norway
    Torsæter, Bendik Nybakk, SINTEF Energy Research AS, Norway
    Abstract
    European and global power grids are moving towards a Smart Grid architecture. Supporting this, advanced measurement equipment such as PQAs and PMUs are being deployed. These generate vast amounts of data upon which machine learning models capable of forecasting incipient faults can be built. We use live measurements from nine PQA nodes in the Norwegian grid to predict incipient interruptions, voltage dips, and earth faults. After training ensembles of gradient boosted decision trees on spectral decompositions of cycle-by-cycle voltage measurements, we evaluate their predictive performance. We find that interruptions are easiest to predict (95 % true positive, 20 % false positives). Earth faults and voltage dips are more challenging. Our models outperform naïve classifiers. We have explored forecast horizons of up to 40 seconds, but we have indications that forecast horizons of at least a few minutes are feasible.
    Publisher
    AIM
    Date
    2019-06-03
    Published in
    • CIRED 2019 Conference
    Permanent link to this record
    https://cired-repository.org/handle/20.500.12455/444
    http://dx.doi.org/10.34890/673
    ISSN
    2032-9644
    ISBN
    978-2-9602415-0-1

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