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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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  3/2024 - 2

A Novel Control Approach Utilizing Neural Network for Efficient Microgrid Operation with Solar PV and Energy Storage Systems

JABBARI, A. See more information about JABBARI, A. on SCOPUS See more information about JABBARI, A. on IEEExplore See more information about JABBARI, A. on Web of Science, KHAN, H. See more information about  KHAN, H. on SCOPUS See more information about  KHAN, H. on SCOPUS See more information about KHAN, H. on Web of Science, MUSHTAQ, D. See more information about  MUSHTAQ, D. on SCOPUS See more information about  MUSHTAQ, D. on SCOPUS See more information about MUSHTAQ, D. on Web of Science, SARWAR, M. See more information about  SARWAR, M. on SCOPUS See more information about  SARWAR, M. on SCOPUS See more information about SARWAR, M. on Web of Science, DURAIBI, S. See more information about  DURAIBI, S. on SCOPUS See more information about  DURAIBI, S. on SCOPUS See more information about DURAIBI, S. on Web of Science, ALMALKI, K. J. See more information about  ALMALKI, K. J. on SCOPUS See more information about  ALMALKI, K. J. on SCOPUS See more information about ALMALKI, K. J. on Web of Science, AHMED, W. See more information about  AHMED, W. on SCOPUS See more information about  AHMED, W. on SCOPUS See more information about AHMED, W. on Web of Science, SIDDIQUI, A. S. See more information about SIDDIQUI, A. S. on SCOPUS See more information about SIDDIQUI, A. S. on SCOPUS See more information about SIDDIQUI, A. S. on Web of Science
 
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Download PDF pdficon (1,730 KB) | Citation | Downloads: 553 | Views: 654

Author keywords
neural network, battery energy storage system, microgrid, DC-DC converter, ANFIS, MPPT

References keywords
power(20), energy(18), systems(14), control(14), microgrid(13), grid(12), management(9), electronics(7), strategy(6), optimal(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2024-08-31
Volume 24, Issue 3, Year 2024, On page(s): 13 - 22
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2024.03002
Web of Science Accession Number: 001306111400002
SCOPUS ID: 85203004430

Abstract
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This article introduces a novel approach for controlling a single-phase grid-connected inverter using neural network technology. While previous studies have primarily focused on voltage control techniques to facilitate power transfer in such systems, this paper advocates for the application of artificial intelligence for enhanced efficiency. Specifically, the proposed control method employs a neural network trained for function approximation to optimize power exchange between the microgrid and the main power grid. To manage battery operations, a bidirectional converter is utilized, ensuring efficient charging and discharging. During grid integration mode, voltage regulation within the microgrid is overseen by the single-phase inverter, whereas boost converters take charge during isolation mode. Results demonstrate a considerable enhancement in power management between the microgrid and the grid, alongside effective voltage regulation of the DC bus.


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References Weight

Web of Science® Citations for all references: 3,189 TCR
SCOPUS® Citations for all references: 4,183 TCR

Web of Science® Average Citations per reference: 91 ACR
SCOPUS® Average Citations per reference: 120 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-12-08 14:58 in 223 seconds.




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