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JCR Impact Factor: 0.650
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Avg review time: 72 days


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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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2019-Dec-16
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Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

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  3/2017 - 8

Research and Implementation of a USB Interfaced Real-Time Power Quality Disturbance Classification System

GOK, M. See more information about GOK, M. on SCOPUS See more information about GOK, M. on IEEExplore See more information about GOK, M. on Web of Science, SEFA, I. See more information about SEFA, I. on SCOPUS See more information about SEFA, I. on SCOPUS See more information about SEFA, I. on Web of Science
 
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 (1,660 KB) | Citation | Downloads: 372 | Views: 618

Author keywords
discrete transforms, graphical user interfaces, neural networks, power quality, real-time system

References keywords
power(46), quality(29), transform(19), system(17), classification(17), disturbances(16), systems(10), detection(10), time(9), real(8)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-08-31
Volume 17, Issue 3, Year 2017, On page(s): 61 - 70
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.03008
Web of Science Accession Number: 000410369500008
SCOPUS ID: 85028532369

Abstract
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In this study, the research and implementation of an automatic power quality (PQ) recognition system are presented. This system contains a USB interfaced multichannel data acquisition (DAQ) device and a graphical user interfaced (GUI) application. The DAQ device consists of an analog-to-digital (ADC) converter, field programmable gate array (FPGA) and a USB first in first out (FIFO) buffer interface chip. The application employs Stockwell Transform (ST) technique combined with neural network model to build the classifier. Eight basic and two combined PQ disturbances are determined for the classification. Different from the previous studies, the synthetic signals used for neural network training are modified by adding the harmonics detected in the real signal. This approach is used to increase the classifier accuracy against the real line power signal. Also, ST is simplified by using only the frequencies which are required in the feature extraction step to reduce the processing time. Developed application handles the signal processing, the classification, and the database recording tasks by using multi-threaded programming approach under the mean time of 41 ms. The experimental results show that the proposed power quality disturbance detection system is capable of recognizing and reporting power quality faults effectively within the real-time requirements.


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Cited-By CrossRef

SCOPUS® Times Cited: 2
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Cited-By CrossRef

[1] Three-Layer Bayesian Network for Classification of Complex Power Quality Disturbances, Luo, Yi, Li, Kaicheng, Li, Yuanzheng, Cai, Delong, Zhao, Chen, Meng, Qingxu, IEEE Transactions on Industrial Informatics, ISSN 1551-3203, Issue 9, Volume 14, 2018.
Digital Object Identifier: 10.1109/TII.2017.2785321
[CrossRef]

[2] Controllable AC/DC Integration for Power Quality Improvement in Microgrids, KARABIBER, A., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 2, Volume 19, 2019.
Digital Object Identifier: 10.4316/AECE.2019.02013
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Stefan cel Mare University of Suceava, Romania


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