Title:	Clustering-based reliability assessment of smart grids by fuzzy c-means algorithm considering direct cyber–physical interdependencies and system uncertainties
Authors:	Memari, Mehran Karimi, Ali Hashemi Dezaki, Hamed
Citation:	MEMARI, M. KARIMI, A. HASHEMI DEZAKI, H. Clustering-based reliability assessment of smart grids by fuzzy c-means algorithm considering direct cyber–physical interdependencies and system uncertainties. Sustainable Energy, Grids and Networks, 2022, roč. 31, č. September 2022, s. 1-24. ISSN: 2352-4677
Issue Date:	2022
Publisher:	Elsevier
Document type:	článek article
URI:	2-s2.0-85130721546 http://hdl.handle.net/11025/49007
ISSN:	2352-4677
Keywords in different language:	smart grid;cyber–power interdependencies;reliability evaluation;fuzzy c-means clustering algorithm;Monte Carlo simulation;uncertainty
Abstract in different language:	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.
Rights:	Plný text je přístupný v rámci univerzity přihlášeným uživatelům. © Elsevier
Appears in Collections:	Články / Articles (RICE) OBD

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