Data driven transformer level misconfiguration detection in power distribution grids

Fellner, David and Strasser, Thomas I. and Kastner, Wolfgang and Feizifar, Behnam and Abdulhadi, Ibrahim F.; (2022) Data driven transformer level misconfiguration detection in power distribution grids. In: 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE International Conference on Systems, Man, and Cybernetics (SMC) . IEEE, Piscataway, NJ., pp. 1840-1847. ISBN 9781665452588 (https://doi.org/10.1109/smc53654.2022.9945534)

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Abstract

As more novel devices are integrated into the electricity grid due to the changes taking place in the energy system, ways of detecting deviations from the intended settings are needed. If misconfigurations of, for example, reactive power control curves of inverters go unnoticed, the safe and reliable operation of the power grid can no longer be ensured due to possible voltage violations or overloadings. Therefore, methods of detection of misconfigurations of said inverters using operational data at transformers are presented and compared. These methods include preprocessing by dimensionality reduction as well as detection by supervised learning approaches. The data used is of high reliability as it was collected in a lab setting reenacting typical and relevant grid operation situations. Furthermore, this data was recreated by simulation to validate the simulation data, which could also potentially be used for detection applications on a bigger scale. The results for both data sources were compared and conclusions drawn about applicability and usability for grid operators.

ORCID iDs

Fellner, David, Strasser, Thomas I., Kastner, Wolfgang, Feizifar, Behnam ORCID logoORCID: https://orcid.org/0000-0003-0583-5800 and Abdulhadi, Ibrahim F. ORCID logoORCID: https://orcid.org/0000-0002-3657-8379;
  • Item type:Book Section
    ID code:83403
    Dates:
    Date
    Event
    18 November 2022
    Published
    12 October 2022
    Published Online
    15 June 2022
    Accepted
    Notes:© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering > Production of electric energy or power
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:05 Dec 2022 11:45
    Last modified:11 Nov 2024 15:31
    URI:https://strathprints.strath.ac.uk/id/eprint/83403
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