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Reliability evaluation of smart grid using various classic and metaheuristic clustering algorithms considering system uncertainties

Identifikátory výsledku

  • Kód výsledku v IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F21%3A43962523" target="_blank" >RIV/49777513:23220/21:43962523 - isvavai.cz</a>

  • Výsledek na webu

    <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/2050-7038.12902" target="_blank" >https://onlinelibrary.wiley.com/doi/full/10.1002/2050-7038.12902</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1002/2050-7038.12902" target="_blank" >10.1002/2050-7038.12902</a>

Alternativní jazyky

  • Jazyk výsledku

    angličtina

  • Název v původním jazyce

    Reliability evaluation of smart grid using various classic and metaheuristic clustering algorithms considering system uncertainties

  • Popis výsledku v původním jazyce

    The reliability of the smart grid is adversely affected due to system uncertainties. Also, the steadily growing deployment of renewable distributed generation (DG) units increases the uncertainties of smart grids. Hence, it is essential to concern the uncertainties in the field of reliability evaluation of smart grids. Although the Monte Carlo simulation (MCS) has received a significant deal of consideration in the literature, there is a research gap in using the clustering algorithms to assess smart grids&apos; reliability. This article aims to fill such a research gap by proposing a new reliability assessment method, using various clustering algorithms. The benefits from the proposed method&apos;s accuracy and fast computation are highlighted, while optimal operation, optimal short-term planning, and repetitive problems should be studied. In this paper, the performance and accuracy of various classic (k-means, fuzzy c-means, and k-medoids) and metaheuristic (genetic algorithm, particle swarm optimization, differential evolutionary, harmony search, and artificial bee colony) clustering algorithms are studied. Comparing different scenario reduction algorithms in the proposed reliability evaluation method is one of the most contributions. he proposed method is applied to two realistic test systems. Test results infer that the proposed method is adequately precise, while the required computation time is less than MCS-based approaches. Test results for both test systems imply that the accurate expected energy not supplied (EENS) with less than 2.1% is achievable applying the proposed method. The fuzzy c-means clustering algorithm results in the best accuracy among the studied classic and nonclassic(metaheuristic) algorithms.

  • Název v anglickém jazyce

    Reliability evaluation of smart grid using various classic and metaheuristic clustering algorithms considering system uncertainties

  • Popis výsledku anglicky

    The reliability of the smart grid is adversely affected due to system uncertainties. Also, the steadily growing deployment of renewable distributed generation (DG) units increases the uncertainties of smart grids. Hence, it is essential to concern the uncertainties in the field of reliability evaluation of smart grids. Although the Monte Carlo simulation (MCS) has received a significant deal of consideration in the literature, there is a research gap in using the clustering algorithms to assess smart grids&apos; reliability. This article aims to fill such a research gap by proposing a new reliability assessment method, using various clustering algorithms. The benefits from the proposed method&apos;s accuracy and fast computation are highlighted, while optimal operation, optimal short-term planning, and repetitive problems should be studied. In this paper, the performance and accuracy of various classic (k-means, fuzzy c-means, and k-medoids) and metaheuristic (genetic algorithm, particle swarm optimization, differential evolutionary, harmony search, and artificial bee colony) clustering algorithms are studied. Comparing different scenario reduction algorithms in the proposed reliability evaluation method is one of the most contributions. he proposed method is applied to two realistic test systems. Test results infer that the proposed method is adequately precise, while the required computation time is less than MCS-based approaches. Test results for both test systems imply that the accurate expected energy not supplied (EENS) with less than 2.1% is achievable applying the proposed method. The fuzzy c-means clustering algorithm results in the best accuracy among the studied classic and nonclassic(metaheuristic) algorithms.

Klasifikace

  • Druh

    J<sub>imp</sub> - Článek v periodiku v databázi Web of Science

  • CEP obor

  • OECD FORD obor

    20201 - Electrical and electronic engineering

Návaznosti výsledku

  • Projekt

  • Návaznosti

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Ostatní

  • Rok uplatnění

    2021

  • Kód důvěrnosti údajů

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

Údaje specifické pro druh výsledku

  • Název periodika

    International Transactions on Electrical Energy Systems

  • ISSN

    2050-7038

  • e-ISSN

  • Svazek periodika

    31

  • Číslo periodika v rámci svazku

    6

  • Stát vydavatele periodika

    US - Spojené státy americké

  • Počet stran výsledku

    28

  • Strana od-do

    1-28

  • Kód UT WoS článku

    000639871700001

  • EID výsledku v databázi Scopus

    2-s2.0-85104251290