Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
SCOPUS CiteScore: 2.0
Issues per year: 4
Current issue: Feb 2024
Next issue: May 2024
Avg review time: 77 days
Avg accept to publ: 48 days
APC: 300 EUR


PUBLISHER

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


TRAFFIC STATS

2,539,106 unique visits
1,009,558 downloads
Since November 1, 2009



Robots online now
Googlebot
bingbot


SCOPUS CiteScore

SCOPUS CiteScore


SJR SCImago RANK

SCImago Journal & Country Rank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 24 (2024)
 
     »   Issue 1 / 2024
 
 
 Volume 23 (2023)
 
     »   Issue 4 / 2023
 
     »   Issue 3 / 2023
 
     »   Issue 2 / 2023
 
     »   Issue 1 / 2023
 
 
 Volume 22 (2022)
 
     »   Issue 4 / 2022
 
     »   Issue 3 / 2022
 
     »   Issue 2 / 2022
 
     »   Issue 1 / 2022
 
 
 Volume 21 (2021)
 
     »   Issue 4 / 2021
 
     »   Issue 3 / 2021
 
     »   Issue 2 / 2021
 
     »   Issue 1 / 2021
 
 
  View all issues  


FEATURED ARTICLE

Analysis of the Hybrid PSO-InC MPPT for Different Partial Shading Conditions, LEOPOLDINO, A. L. M., FREITAS, C. M., MONTEIRO, L. F. C.
Issue 2/2022

AbstractPlus






LATEST NEWS

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.

2021-Jun-30
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

Read More »


    
 

  4/2021 - 1
View TOC | « Previous Article | Next Article »

 HIGHLY CITED PAPER 

A Wind Energy Prediction Scheme Combining Cauchy Variation and Reverse Learning Strategy

WU, X. See more information about WU, X. on SCOPUS See more information about WU, X. on IEEExplore See more information about WU, X. on Web of Science, SHEN, X. See more information about  SHEN, X. on SCOPUS See more information about  SHEN, X. on SCOPUS See more information about SHEN, X. on Web of Science, ZHANG, J. See more information about  ZHANG, J. on SCOPUS See more information about  ZHANG, J. on SCOPUS See more information about ZHANG, J. on Web of Science, ZHANG, Y. See more information about ZHANG, Y. on SCOPUS See more information about ZHANG, Y. on SCOPUS See more information about ZHANG, Y. on Web of Science
 
View the paper record and citations in View the paper record and citations in Google Scholar
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,966 KB) | Citation | Downloads: 1,449 | Views: 1,638

Author keywords
carbon emissions, cauchy mutation, long short-term memory, reverse learning, synchrosqueezed wavelet transforms

References keywords
wind(18), energy(18), speed(13), forecasting(11), prediction(9), term(8), short(8), model(7), zhao(6), novel(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2021-11-30
Volume 21, Issue 4, Year 2021, On page(s): 3 - 10
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2021.04001
Web of Science Accession Number: 000725107100001
SCOPUS ID: 85122245524

Abstract
Quick view
Full text preview
Modular multilevel converters (MMCs) can be a reliable solution since they have modular structure and high quality output waveform for permanent magnet synchronous generator (PMSG) based wind energy conversion system (WECS). Capacitor voltage balancing in nearest level modulation (NLM) is required to keep the capacitor voltage of each submodule of MMC constant. In this paper, an efficient capacitor voltage balancing scheme under NLM is proposed for PMSG based WECS with MMC topology. Through proposed control scheme, arm voltages are separately controlled and voltage ripple of around 1.5% is obtained. This result provides high quality output waveform at the point of common coupling (PCC). Furthermore, DC-link voltage control is achieved via hysteresis current control based proportional-integral controller. The ripple of DC-link voltage is obtained quite well as nearly 0.25%. In addition, load voltage control is accomplished using dq reference frame-based voltage control scheme for voltage and frequency stabilization at the PCC by regulating the voltage at its reference value. Simulation studies show that all proposed control schemes give satisfactory results for MMC based WECS under variable dynamic operation modes. Finally, experimental verification is performed using laboratory prototype to show the applicability of the proposed capacitor voltage balancing scheme.


References | Cited By  «-- Click to see who has cited this paper

[1] Y. Zhang, Y. Li, G. Zhang, "Short-term wind power forecasting approach based on Seq2Seq model using NWP data," Energy, 2020: 118371.
[CrossRef] [Web of Science Times Cited 114]


[2] H. Shuai, X. Yue, H. Zhang, S. Xie, J. Li, C. Gu, W. Sun, J. Liu,. "Hybrid forecasting method for wind power integrating spatial correlation and corrected numerical weather prediction," Applied Energy, 2021, 293: 116951.
[CrossRef] [Web of Science Times Cited 51]


[3] E. Erdem, J. Shi. "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, 2011, 88(4): 1405-1414.
[CrossRef] [Web of Science Times Cited 615]


[4] W. Li, X. Jia, X. Li,, Y. Wang, J. Lee, "A Markov model for short term wind speed prediction by integrating the wind acceleration information," Renewable Energy, 2021, 164: 242-253.
[CrossRef] [Web of Science Times Cited 25]


[5] J. Zhou, J. Shi, G. Li, "Fine tuning support vector machines for short-term wind speed forecasting," Energy Conversion and Management, 2011, 52(4): 1990-1998.
[CrossRef] [Web of Science Times Cited 298]


[6] D. Putz, M. Gumhalter, H. Auer, "A novel approach to multi-horizon wind power forecasting based on deep neural architecture," Renewable Energy, 2021, 178: 494-505.
[CrossRef] [Web of Science Times Cited 34]


[7] Y. Zhang, Y. Zhao, X. Shen, J. Zhang, "A comprehensive wind speed prediction system based on Monte Carlo and artificial intelligence algorithms," Applied Energy, 2022, 305: 117815.
[CrossRef] [Web of Science Times Cited 51]


[8] S. Wang, N. Zhang, L. Wu, Y. Wang, "Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method" Renewable Energy, 2016, 94: 629-636.
[CrossRef] [Web of Science Times Cited 486]


[9] X. J. Chen, J. Zhao, X. Z. Jia, Z. L. Li, "Multi-step wind speed forecast based on sample clustering and an optimized hybrid system," Renewable Energy, 2021, 165: 595-611.
[CrossRef] [Web of Science Times Cited 32]


[10] Z. Preitl, R. E. Precup, J. K. Tar, M. Takács, "Use of multi-parametric quadratic programming in fuzzy control systems," Acta Polytechnica Hungarica, 2006, 3(3): 29-43

[11] R. E. Precup, R. C. David, R. C. Roman, A. I. Szedlak-Stinean, E. M. Petriu, "Optimal tuning of interval type-2 fuzzy controllers for nonlinear servo systems using Slime Mould Algorithm," International Journal of Systems Science, 2021: 1-16.
[CrossRef] [Web of Science Times Cited 86]


[12] M. Moattari, M. H. Moradi, "Conflict monitoring optimization heuristic inspired by brain fear and conflict systems," Int J Artif Intell, 2020, 18(1): 45-62

[13] C. Song, L. Yao, C. Hua, Q. Ni, "A novel hybrid model for water quality prediction based on synchrosqueezed wavelet transform technique and improved long short-term memory," Journal of Hydrology, 2021, 603(Part A): 126879.
[CrossRef] [Web of Science Times Cited 47]


[14] Q. Mao, Q. Zhang, "Improved Sparrow algorithm integrating cauchy mutation and reverse learning," Journal of Frontiers of Computer Science and Technology, 2020, 15(6): 1155-1164.
[CrossRef]


[15] G. Memarzadeh, F. Keynia, "A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets," Energy Conversion and Management, 2020, 213: 112824.
[CrossRef] [Web of Science Times Cited 178]


[16] Y. Zhang, G. Pan, Y. Zhao, Q. Li, F. Wang, "Short-term wind speed interval prediction based on artificial intelligence methods and error probability distribution," Energy Conversion and Management, 2020, 224: 1-14.
[CrossRef] [Web of Science Times Cited 49]


[17] A. Glowacz, "Ventilation diagnosis of angle grinder using thermal imaging" Sensors 2021; 21:2853.
[CrossRef] [Web of Science Times Cited 130]


[18] Y. Zhao, W. Zhang, X. Gong, C. Wang, "A novel method for online real-time forecasting of crude oil price," Applied Energy, 2021; 303: 117588.
[CrossRef] [Web of Science Times Cited 31]


[19] Y. Nie, N. Liang, J. Wang, "Ultra-short-term wind-speed bi-forecasting system via artificial intelligence and a double-forecasting scheme," Applied Energy, 2021, 301: 117452.
[CrossRef] [Web of Science Times Cited 42]


[20] T. Liang, Q., Zhao, Q. Lv, H. Sun, "A novel wind speed prediction strategy based on Bi-LSTM, MOOFADA and transfer learning for centralized control centers," Energy, 2021, 230: 120904.
[CrossRef] [Web of Science Times Cited 63]


[21] B. Lin, C. Zhang, "A novel hybrid machine learning model for short-term wind speed prediction in inner Mongolia, China," Renewable Energy, 2021.
[CrossRef] [Web of Science Times Cited 15]


[22] W. Shuai, W. Jianzhou, H. Lu, W. Zhao, "A novel combined model for wind speed prediction-Combination of linear model, shallow neural networks, and deep learning approaches," Energy, 2021, 234: 121275.
[CrossRef] [Web of Science Times Cited 64]


[23] Y. Zhang, G. Pan, "A hybrid prediction model for forecasting wind energy resources," Environmental Science and Pollution Research, 2020, 27(16): 19428-19446.
[CrossRef] [Web of Science Times Cited 22]


[24] A. Glowacz, "Fault diagnosis of electric impact drills using thermal imaging," Measurement 2021; 171:108815.
[CrossRef] [Web of Science Times Cited 171]


[25] T. B. M. J. Ouarda, C. Charron, "Non-stationary statistical modelling of wind speed: A case study in eastern Canada," Energy Conversion and Management, 2021, 236: 114028.
[CrossRef] [Web of Science Times Cited 15]




References Weight

Web of Science® Citations for all references: 2,619 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 101 ACR
SCOPUS® Average Citations per reference: 0

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 2024-04-22 19:14 in 131 seconds.




Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

Copyright ©2001-2024
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.

Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.




Website loading speed and performance optimization powered by: 


DNS Made Easy