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Clustering-based reliability assessment of smart grids by fuzzy c-means algorithm considering direct cyber–physical interdependencies and system uncertainties

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F22%3A43965208" target="_blank" >RIV/49777513:23220/22:43965208 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.sciencedirect.com/science/article/pii/S2352467722000819" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2352467722000819</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.segan.2022.100757" target="_blank" >10.1016/j.segan.2022.100757</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Clustering-based reliability assessment of smart grids by fuzzy c-means algorithm considering direct cyber–physical interdependencies and system uncertainties

  • Original language description

    The steadily growing deployment of cyber systems in smart grids (SGs) has highlighted the impacts of cyber–physical interdependencies (CPIs). Although much attention has been paid to the reliability evaluation of SGs considering the system uncertainties and CPIs by Monte Carlo simulation (MCS), the computation time is one of the essential challenges of MCS-based methods. This research tries to overcome the discussed challenge by developing a new clustering-based reliability evaluation method considering the direct CPIs (DCPIs) and stochastic behaviors of renewable distributed generation units (RDGUs) and the demand side. In the proposed method, the Fuzzy c-means (FCM) clustering algorithm has been used to reduce the number of scenarios for uncertain parameters besides the DCPIs. Determining the appropriate alternatives for the number of clusters of stochastic parameters in various cases based on cyber network topologies, DG technologies, and the penetration levels of RDGUs is another contribution of this paper. Test results of applying the proposed method to an actual test system illustrate the advantages of the proposed clustering-based method. The comparison of the proposed method with MCS shows the computation time could be reduced from 21658 s to 210 s (99%), while less than 1% EENS error appears.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20201 - Electrical and electronic engineering

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2022

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Sustainable Energy, Grids and Networks

  • ISSN

    2352-4677

  • e-ISSN

  • Volume of the periodical

    31

  • Issue of the periodical within the volume

    September 2022

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    24

  • Pages from-to

    1-24

  • UT code for WoS article

    000807419100014

  • EID of the result in the Scopus database

    2-s2.0-85130721546