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Online Learning-based Islanding Detection Scheme for Grid-Connected Systems

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU145957" target="_blank" >RIV/00216305:26220/22:PU145957 - isvavai.cz</a>

  • Result on the web

    <a href="https://ieeexplore.ieee.org/document/9907714" target="_blank" >https://ieeexplore.ieee.org/document/9907714</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Online Learning-based Islanding Detection Scheme for Grid-Connected Systems

  • Original language description

    Data aggregation in smart grids is a key component for emergency responses during abnormalities in the grid. To efficiently utilize the aggregated data, and achieve fast identification of these abnormalities, this paper develops an online islanding detection approach. The development of the technique is realized with an online learning algorithm implemented using the large-scale support vector machine (LaSVM). The algorithm adopts a classification problem for islanding detection in grid-connected systems by considering a set of independent variables and unknown variables. The independent variables are related to the known islanding events in the grid-connected system, and the unknown variables are related to the dynamics of the grid operating in real-time. The proposed approach solves this problem by training the known and unknown variables and identifying new instances through sequential minimal optimization. The training and validation results provided indicate 99.8 % accuracy for islanding detection under standard operating conditions of the grid-connected system.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20201 - Electrical and electronic engineering

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    2022 24th European Conference on Power Electronics and Applications (EPE'22 ECCE Europe)

  • ISBN

    978-9-0758-1539-9

  • ISSN

  • e-ISSN

  • Number of pages

    10

  • Pages from-to

    1-10

  • Publisher name

    Neuveden

  • Place of publication

    neuveden

  • Event location

    Hannover

  • Event date

    Sep 5, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article