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
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Czech description
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Classification
Type
J<sub>imp</sub> - 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
Result continuities
Project
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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
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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