Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 1.221
JCR 5-Year IF: 0.961
SCOPUS CiteScore: 2.5
Issues per year: 4
Current issue: May 2021
Next issue: Aug 2021
Avg review time: 91 days


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

1,681,904 unique visits
543,691 downloads
Since November 1, 2009



Robots online now
PetalBot
Googlebot
bingbot


SJR SCImago RANK

SCImago Journal & Country Rank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 21 (2021)
 
     »   Issue 2 / 2021
 
     »   Issue 1 / 2021
 
 
 Volume 20 (2020)
 
     »   Issue 4 / 2020
 
     »   Issue 3 / 2020
 
     »   Issue 2 / 2020
 
     »   Issue 1 / 2020
 
 
 Volume 19 (2019)
 
     »   Issue 4 / 2019
 
     »   Issue 3 / 2019
 
     »   Issue 2 / 2019
 
     »   Issue 1 / 2019
 
 
 Volume 18 (2018)
 
     »   Issue 4 / 2018
 
     »   Issue 3 / 2018
 
     »   Issue 2 / 2018
 
     »   Issue 1 / 2018
 
 
 Volume 17 (2017)
 
     »   Issue 4 / 2017
 
     »   Issue 3 / 2017
 
     »   Issue 2 / 2017
 
     »   Issue 1 / 2017
 
 
  View all issues  








LATEST NEWS

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.

2021-Jun-06
SCOPUS published the CiteScore for 2020, computed by using an improved methodology, counting the citations received in 2017-2020 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering in 2020 is 2.5, better than all our previous results.

2021-Apr-15
Release of the v3 version of AECE Journal website. We moved to a new server and implemented the latest cryptographic protocols to assure better compatibility with the most recent browsers. Our website accepts now only TLS 1.2 and TLS 1.3 secure connections.

2020-Jun-29
Clarivate Analytics published the InCites Journal Citations Report for 2019. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.102 (1.023 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.734.

2020-Jun-11
Starting on the 15th of June 2020 we wiil introduce a new policy for reviewers. Reviewers who provide timely and substantial comments will receive a discount voucher entitling them to an APC reduction. Vouchers (worth of 25 EUR or 50 EUR, depending on the review quality) will be assigned to reviewers after the final decision of the reviewed paper is given. Vouchers issued to specific individuals are not transferable.

Read More »


    
 

  2/2021 - 10

An Efficient and High-Speed Disturbance Detection Algorithm Design with Emphasis on Operation of Static Transfer Switch

USMAN, A. See more information about USMAN, A. on SCOPUS See more information about USMAN, A. on IEEExplore See more information about USMAN, A. on Web of Science, CHOUDHRY, M. A. See more information about CHOUDHRY, M. A. on SCOPUS See more information about CHOUDHRY, M. A. on SCOPUS See more information about CHOUDHRY, M. A. 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 (1,519 KB) | Citation | Downloads: 253 | Views: 137

Author keywords
power quality, power system, event detection, feature extraction, support vector machine

References keywords
power(47), quality(28), detection(21), classification(21), transfer(17), disturbances(17), switch(15), systems(13), static(13), voltage(11)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2021-05-31
Volume 21, Issue 2, Year 2021, On page(s): 87 - 98
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2021.02010
Web of Science Accession Number: 000657126200010

Abstract
Quick view
Full text preview
Static Transfer Switch (STS) is required for high-speed transfer of essential load to the alternate power source when the main source fails due to power disturbance (PD). A fast and accurate PD detection method is required to ensure transfer time recommended by Computer Business Equipment Manufacturers Association (CBEMA) and IEEE Std. 446. This study encompasses the machine learning technique to reduce detection time for the disturbance on the preferred source. The 10 sample frames of acquired voltage signal were first differentiated and then distinctive features, i.e., Mean Absolute Deviation (MAD) and Energy (E) were extracted from the resultant frames. The features were fed to the Linear Support Vector Machine (L-SVM) classifier to detect the occurrence of PD events. The proposed approach achieved 100% accuracy for PD detection and detection time was significantly reduced. The system is robust in terms of unbalanced and marginal PDs. The system was validated using both simulated and real voltage signals. The proposed algorithm is easy to implement on an embedded system. Hence, detection time according to STS requirements can be achieved under various power system conditions.


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

[1] A. A. Abdoos, P. K. Mianaei, and M. R. Ghadikolaei, "Combined VMD-SVM based feature selection method for classification of power quality events," Applied Soft Computing, vol. 38, pp. 637-646, Oct. 2016.
[CrossRef] [Web of Science Times Cited 88] [SCOPUS Times Cited 96]


[2] H. Mokhtari, "High speed silicon controlled rectifier static transfer switch," National Library of Canada = Bibliotheque nationale du Canada, 2000

[3] H. Mokhtari, M. Iravani, S. Dewan, P. Lehn, and J. Martinez, "Benchmark systems for digital computer simulation of a static transfer switch," IEEE Power Engineering Review, vol. 21, pp. 69-69, Jul. 2001.
[CrossRef]


[4] M. Moschakis and N. Hatziargyriou, "A detailed model for a thyristor-based static transfer switch," IEEE Transactions on Power Delivery, vol. 18, pp. 1442-1449, Oct. 2003.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 23]


[5] C. He, F. Li, and V. Sood, "Static transfer switch (STS) model in EMTPWorks RV," in Canadian Conference on Electrical and Computer Engineering 2004 (IEEE Cat. No. 04CH37513), 2004, Niagara Falls, pp. 111-116.
[CrossRef]


[6] S.-M. Song, J.-Y. Kim, and I.-D. Kim, "New Three-Phase Static Transfer Switch using AC SSCB," in 2018 International Power Electronics Conference (IPEC-Niigata 2018-ECCE Asia), 2018, Niigata, pp. 3229-3236.
[CrossRef] [SCOPUS Times Cited 3]


[7] S. C. Yanez-Campos, G. Cerda-Villafana, and J. M. Lozano-Garcia, "Two-Feeder Dynamic Voltage Restorer for Application in Custom Power Parks," Energies, vol. 12, p. 3248, Aug. 2019.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 4]


[8] J. Schwartzenberg and R. De Doncker, "15 kV medium voltage static transfer switch," in IAS'95. Conference Record of the 1995 IEEE Industry Applications Conference Thirtieth IAS Annual Meeting, 1995, Orlando, pp. 2515-2520.
[CrossRef]


[9] M. Faisal, M. A. Hannan, P. J. Ker, M. S. B. A. Rahman, M. S. Mollik, and M. B. Mansur, "Review of Solid State Transfer Switch on Requirements, Standards, Topologies, Control, and Switching Mechanisms: Issues and Challenges," Electronics, vol. 9, p. 1396, Aug. 2020.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 2]


[10] H. Mokhtari, S. B. Dewan, and M. Travani, "Performance evaluation of thyristor based static transfer switch," IEEE Transactions on Power Delivery, vol. 15, pp. 960-966, Jul. 2000.
[CrossRef] [Web of Science Times Cited 38] [SCOPUS Times Cited 47]


[11] H. Mokhtari, S. B. Dewan, and M. R. Iravani, "Effect of regenerative load on a static transfer switch performance," IEEE transactions on power delivery, vol. 16, pp. 619-624, Oct. 2001.
[CrossRef] [Web of Science Times Cited 24] [SCOPUS Times Cited 28]


[12] H. Mokhtari, "Performance evaluation of thyristor-based static transfer switch with respect to cross current," in IEEE/PES Transmission and Distribution Conference and Exhibition, 2002, Yokohama, pp. 1326-1331.
[CrossRef]


[13] H. Mokhtari, S. B. Dewan, and M. R. Iravani, "Analysis of a static transfer switch with respect to transfer time," IEEE Transactions on Power Delivery, vol. 17, pp. 190-199, Nov. 2001.
[CrossRef]


[14] A. Sannino, "Static transfer switch: analysis of switching conditions and actual transfer time," in 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No. 01CH37194), 2001, Columbus, pp. 120-125.
[CrossRef]


[15] H. Mokhtari and M. R. Iravani, "Effect of source phase difference on static transfer switch performance," IEEE transactions on power delivery, vol. 22, pp. 1125-1131, Apr. 2007.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 15]


[16] H. Mokhtari and M. R. Iravani, "Impact of difference of feeder impedances on the performance of a static transfer switch," IEEE transactions on power delivery, vol. 19, pp. 679-685, Mar. 2004.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 9]


[17] R. K. Pachar, H. Tiwari, and R. C. Bansal, "A decisive evaluation of parks transformation based commonly used voltage detection method," 2014

[18] B. Tian, C. Mao, J. Lu, D. Wang, Y. He, Y. Duan, et al., "400 V/1000 kVA hybrid automatic transfer switch," IEEE Transactions on Industrial Electronics, vol. 60, pp. 5422-5435, Jan. 2013.
[CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 24]


[19] F. R. Zaro, S. O. Al-Takrouri, and M. A. Abido, "Efficient on-line detection scheme of voltage events using quadrature method," Sep. 2018.
[CrossRef]


[20] F. Dekhandji, "Signal processing deployment in power quality disturbance detection and classification," Acta Phys Pol, A, vol. 132, pp. 415-419, 2017.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 5]


[21] M. B. Latran and A. Teke, "A novel wavelet transform based voltage sag/swell detection algorithm," International Journal of Electrical Power & Energy Systems, vol. 71, pp. 131-139, Oct. 2015.
[CrossRef] [Web of Science Times Cited 50] [SCOPUS Times Cited 60]


[22] F. Z. Dekhandji, "Detection of power quality disturbances using discrete wavelet transform," in 2017 5th International Conference on Electrical Engineering-Boumerdes (ICEE-B), 2017, Boumerdes, pp. 1-5.
[CrossRef] [SCOPUS Times Cited 12]


[23] F. Ucar, O. F. Alcin, B. Dandil, and F. Ata, "Power quality event detection using a fast extreme learning machine," Energies, vol. 11, p. 145, Jan. 2018.
[CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 29]


[24] M. Lopez-Ramirez, E. Cabal-Yepez, L. M. Ledesma-Carrillo, H. Miranda-Vidales, C. Rodriguez-Donate, and R. A. Lizarraga-Morales, "FPGA-based online PQD detection and classification through DWT, mathematical morphology and SVD," Energies, vol. 11, p. 769, Mar. 2018.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 9]


[25] J. Li, Z. Teng, Q. Tang, and J. Song, "Detection and classification of power quality disturbances using double resolution S-transform and DAG-SVMs," IEEE Transactions on Instrumentation and Measurement, vol. 65, pp. 2302-2312, Jun. 2016.
[CrossRef] [Web of Science Times Cited 94] [SCOPUS Times Cited 116]


[26] R. Kapoor, R. Gupta, S. Jha, and R. Kumar, "Boosting performance of power quality event identification with KL divergence measure and standard deviation," Measurement, vol. 126, pp. 134-142, Oct. 2018.
[CrossRef] [Web of Science Times Cited 27] [SCOPUS Times Cited 30]


[27] A. Shaik and A. S. Reddy, "Flexible entropy based feature selection and multi class SVM for detection and classification of power quality disturbances," International Journal of Intelligent Engineering and Systems, vol. 11, Apr. 2018.
[CrossRef] [SCOPUS Times Cited 2]


[28] T. Zhong, S. Zhang, G. Cai, Y. Li, B. Yang, and Y. Chen, "Power quality disturbance recognition based on multiresolution s-transform and decision tree," IEEE Access, vol. 7, pp. 88380-88392, Jun. 2019.
[CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 21]


[29] K. Thirumala, S. Pal, T. Jain, and A. C. Umarikar, "A classification method for multiple power quality disturbances using EWT based adaptive filtering and multiclass SVM," Neurocomputing, vol. 334, pp. 265-274, Mar. 2019.
[CrossRef] [Web of Science Times Cited 20] [SCOPUS Times Cited 29]


[30] O. Jeba Singh, D. Prince Winston, B. Chitti Babu, S. Kalyani, B. Praveen Kumar, M. Saravanan, et al., "Robust detection of real-time power quality disturbances under noisy condition using FTDD features," Automatika, vol. 60, pp. 11-18, Jan. 2019.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 6]


[31] D. De Yong, S. Bhowmik, and F. Magnago, "An effective power quality classifier using wavelet transform and support vector machines," Expert Systems with Applications, vol. 42, pp. 6075-6081, Sep. 2015.
[CrossRef] [Web of Science Times Cited 77] [SCOPUS Times Cited 102]


[32] M. Lopez-Ramirez, L. Ledesma-Carrillo, E. Cabal-Yepez, C. Rodriguez-Donate, H. Miranda-Vidales, and A. Garcia-Perez, "EMD-based feature extraction for power quality disturbance classification using moments," Energies, vol. 9, p. 565, Jul. 2016.
[CrossRef] [Web of Science Times Cited 17] [SCOPUS Times Cited 18]


[33] R. Kumar, B. Singh, and D. T. Shahani, "Recognition of single-stage and multiple power quality events using Hilbert-Huang transform and probabilistic neural network," Electric Power Components and Systems, vol. 43, pp. 607-619, Mar. 2015.
[CrossRef] [Web of Science Times Cited 28] [SCOPUS Times Cited 35]


[34] M. Gok and I. Sefa, "Research and implementation of a USB interfaced real-time power quality disturbance classification system," Advances in Electrical and Computer Engineering, vol. 17, pp. 61-71, Aug. 2017.
[CrossRef] [Full Text] [Web of Science Times Cited 9] [SCOPUS Times Cited 9]


[35] M. I. Gursoy, A. S. Yilmaz, and S. V. Ustun, "A practical real-time power quality event monitoring applications using discrete wavelet transform and artificial neural network," Journal of Engineering Science and Technology (JESTEC), vol. 13, pp. 1764-1781, 2018

[36] O. I. Abiodun, A. Jantan, A. E. Omolara, K. V. Dada, A. M. Umar, O. U. Linus, et al., "Comprehensive review of artificial neural network applications to pattern recognition," IEEE Access, vol. 7, pp. 158820-158846, Oct. 2019.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 28]


[37] A. K. Sadigh and K. Smedley, "Fast and precise voltage sag detection method for dynamic voltage restorer (DVR) application," Electric Power Systems Research, vol. 130, pp. 192-207, Jan. 2016.
[CrossRef] [Web of Science Times Cited 32] [SCOPUS Times Cited 42]


[38] M. S. Manikandan, S. Samantaray, and I. Kamwa, "Detection and classification of power quality disturbances using sparse signal decomposition on hybrid dictionaries," IEEE Transactions on Instrumentation and Measurement, vol. 64, pp. 27-38, Jun. 2014.
[CrossRef] [Web of Science Times Cited 102] [SCOPUS Times Cited 128]


[39] R. Kumar, B. Singh, and D. Shahani, "Symmetrical components-based modified technique for power-quality disturbances detection and classification," IEEE Transactions on Industry Applications, vol. 52, pp. 3443-3450, Mar. 2016.
[CrossRef] [Web of Science Times Cited 35] [SCOPUS Times Cited 46]


[40] J. Ma, J. Zhang, L. Xiao, K. Chen, and J. Wu, "Classification of power quality disturbances via deep learning," IETE Technical Review, vol. 34, pp. 408-415, Jul. 2016.
[CrossRef] [Web of Science Times Cited 22] [SCOPUS Times Cited 30]


[41] E. A. Nagata, D. D. Ferreira, C. A. Duque, and A. S. Cequeira, "Voltage sag and swell detection and segmentation based on independent component analysis," Electric Power Systems Research, vol. 155, pp. 274-280, Feb. 2018.
[CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 21]


[42] V. A. Katic and A. M. Stanisavljevic, "Smart detection of voltage dips using voltage harmonics footprint," IEEE Transactions on Industry Applications, vol. 54, pp. 5331-5342, Mar. 2018.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 17]


[43] V. A. Katic, A. M. Stanisavljevic, R. L. Turovic, B. P. Dumnic, and B. P. Popadic, "Extended Kalman filter for voltage dips detection in grid with distributed energy resources," in 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 2018, Sarajevo, pp. 1-6.
[CrossRef] [SCOPUS Times Cited 2]


[44] P. N. Kumawat, D. K. Verma, and N. Zaveri, "Comparison between wavelet packet transform and M-band wavelet packet transform for identification of power quality disturbances," Power Research, vol. 14, pp. 37-45, Jun. 2018.
[CrossRef]


[45] W. Qiu, Q. Tang, J. Liu, Z. Teng, and W. Yao, "Power quality disturbances recognition using modified s transform and parallel stack sparse auto-encoder," Electric Power Systems Research, vol. 174, p. 105876, Sep. 2019.
[CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 21]


[46] H. Wang, J. Liu, S. Luo, and X. Xu, "Research on power quality disturbance detection method based on improved ensemble empirical mode decomposition," Electronics, vol. 9, p. 585, Mar. 2020.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 2]


[47] E. G. Ribeiro, T. M. Mendes, G. L. Dias, E. R. Faria, F. M. Viana, B. H. Barbosa, et al., "Real-time system for automatic detection and classification of single and multiple power quality disturbances," Measurement, vol. 128, pp. 276-283, Nov. 2018.
[CrossRef] [Web of Science Times Cited 28] [SCOPUS Times Cited 32]


[48] S. Jamali, A. R. Farsa, and N. Ghaffarzadeh, "Identification of optimal features for fast and accurate classification of power quality disturbances," Measurement, vol. 116, pp. 565-574, Feb. 2018.
[CrossRef] [Web of Science Times Cited 30] [SCOPUS Times Cited 40]


[49] U. Singh and S. N. Singh, "A new optimal feature selection scheme for classification of power quality disturbances based on ant colony framework," Applied Soft Computing, vol. 74, pp. 216-225, Jan. 2019.
[CrossRef] [Web of Science Times Cited 20] [SCOPUS Times Cited 23]


[50] H. Eristi, A. Ucar, and Y. Demir, "Wavelet-based feature extraction and selection for classification of power system disturbances using support vector machines," Electric power systems research, vol. 80, pp. 743-752, Jul. 2010.
[CrossRef] [Web of Science Times Cited 85] [SCOPUS Times Cited 103]


[51] F. A. Borges, R. A. Fernandes, I. N. Silva, and C. B. Silva, "Feature extraction and power quality disturbances classification using smart meters signals," IEEE Transactions on Industrial Informatics, vol. 12, pp. 824-833, Oct. 2015.
[CrossRef] [Web of Science Times Cited 99] [SCOPUS Times Cited 116]


[52] H. Eristi and Y. Demir, "An efficient feature extraction method for classification of power quality disturbances," in Int. Conf. on Power Systems Transients (IPST2009), in Kyoto, Japan, 2009

[53] S. Aziz, M. Awais, T. Akram, U. Khan, M. Alhussein, and K. Aurangzeb, "Automatic scene recognition through acoustic classification for behavioral robotics," Electronics, vol. 8, p. 483, Apr. 2019.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 28]


[54] S. Aziz, M. U. Khan, Z. A. Choudhry, A. Aymin, and A. Usman, "ECG-based biometric authentication using empirical mode decomposition and support vector machines," in 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2019, Vancouver, pp. 0906-0912.
[CrossRef] [SCOPUS Times Cited 31]


[55] M. U. Khan, S. Aziz, S. Ibraheem, A. Butt, and H. Shahid, "Characterization of term and preterm deliveries using electrohysterograms signatures," in 2019 IEEE 10th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2019, Vancouver, pp. 0899-0905.
[CrossRef] [SCOPUS Times Cited 29]


[56] M. U. Khan, S. Aziz, K. Iqtidar, A. Zainab, and A. Saud, "Prediction of acute coronary syndrome using pulse plethysmograph," in 2019 4th International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST), 2019, Karachi, pp. 1-6.
[CrossRef] [SCOPUS Times Cited 21]


[57] M. U. Khan, S. Aziz, A. Malik, and M. A. Imtiaz, "Detection of myocardial infarction using pulse plethysmograph signals," in 2019 International Conference on Frontiers of Information Technology (FIT), 2019, Islamabad, pp. 95-955.
[CrossRef] [SCOPUS Times Cited 20]


[58] T. Andrews, "Computation time comparison between Matlab and C++ using launch windows," 2012.

[59] H. Yu and B. M. Wilamowski, "C++ implementation of neural networks trainer," in 2009 International Conference on Intelligent Engineering Systems, 2009, Barbados, pp. 257-262.
[CrossRef] [SCOPUS Times Cited 21]


[60] W. L. Rodrigues Junior, F. A. Borges, R. d. A. Rabelo, J. J. Rodrigues, R. A. Fernandes, and I. N. da Silva, "A methodology for detection and classification of power quality disturbances using a real‐time operating system in the context of home energy management systems," International Journal of Energy Research, 2020

[61] M. R. Al Koutayni, V. Rybalkin, J. Malik, A. Elhayek, C. Weis, G. Reis, et al., "Real-time energy efficient hand pose estimation: A Case Study," Sensors, vol. 20, p. 2828, May 2020.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 1]


[62] M. Mishra, "Power quality disturbance detection and classification using signal processing and soft computing techniques: A comprehensive review," International Transactions on Electrical Energy Systems, vol. 29, p. e12008, Mar. 2019.
[CrossRef] [Web of Science Times Cited 24] [SCOPUS Times Cited 39]


[63] M. U. Khan, S. Aziz, M. Bilal, and M. B. Aamir, "Classification of EMG signals for assessment of neuromuscular disorder using empirical mode decomposition and logistic regression," in 2019 International Conference on Applied and Engineering Mathematics (ICAEM), 2019, Islamabad, pp. 237-243.
[CrossRef] [SCOPUS Times Cited 20]




References Weight

Web of Science® Citations for all references: 1,152 TCR
SCOPUS® Citations for all references: 1,595 TCR

Web of Science® Average Citations per reference: 18 ACR
SCOPUS® Average Citations per reference: 25 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 2021-07-21 14:25 in 365 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 ©2001-2021
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: