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

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>

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

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20201 - Electrical and electronic engineering

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

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

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