Backward neural network (BNN) based multilevel control for enhancing the quality of an islanded RES DC microgrid under variable communication network
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27730%2F24%3A10255364" target="_blank" >RIV/61989100:27730/24:10255364 - isvavai.cz</a>
Result on the web
<a href="https://www.cell.com/heliyon/fulltext/S2405-8440(24)08677-8" target="_blank" >https://www.cell.com/heliyon/fulltext/S2405-8440(24)08677-8</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.heliyon.2024.e32646" target="_blank" >10.1016/j.heliyon.2024.e32646</a>
Alternative languages
Result language
angličtina
Original language name
Backward neural network (BNN) based multilevel control for enhancing the quality of an islanded RES DC microgrid under variable communication network
Original language description
Microgrids (MGs) and energy communities have been widely implemented, leading to the participation of multiple stakeholders in distribution networks. Insufficient information infrastructure, particularly in rural distribution networks, is leading to a growing number of operational blind areas in distribution networks. An optimization challenge is addressed in multi -feeder microgrid systems to handle load sharing and voltage management by implementing a backward neural network (BNN) as a robust control approach. The control technique consists of a neural network that optimizes the control strategy to calculate the operating directions for each distributed generating point. Neural networks improve control during communication connectivity issues to ensure the computation of operational directions. Traditional control of DC microgrids is susceptible to communication link delays. The proposed BNN technique can be expanded to encompass the entire multi -feeder network for precise load distribution and voltage management. The BNN results are achieved through mathematical analysis of different load conditions and uncertain line characteristics in a radial network of a multi -feeder microgrid, demonstrating the effectiveness of the proposed approach. The proposed BNN technique is more effective than conventional control in accurately distributing the load and regulating the feeder voltage, especially during communication failure.
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
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
<a href="/en/project/TN02000025" target="_blank" >TN02000025: National Centre for Energy II</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Heliyon
ISSN
2405-8440
e-ISSN
2405-8440
Volume of the periodical
10
Issue of the periodical within the volume
12
Country of publishing house
GB - UNITED KINGDOM
Number of pages
16
Pages from-to
nestránkováno
UT code for WoS article
001258370900001
EID of the result in the Scopus database
2-s2.0-85196004339