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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
Current issue: Aug 2017
Next issue: Nov 2017
Avg review time: 77 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


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MOST RECENT ISSUES

 Volume 17 (2017)
 
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FEATURED ARTICLE

Wind Speed Prediction with Wavelet Time Series Based on Lorenz Disturbance, ZHANG, Y., WANG, P., CHENG, P., LEI, S.
Issue 3/2017

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LATEST NEWS

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Apr-04
We have the confirmation Advances in Electrical and Computer Engineering will be included in the EBSCO database.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

2017-Jan-30
We have the confirmation Advances in Electrical and Computer Engineering will be included in the Gale database.

2016-Dec-17
IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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  3/2017 - 8

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

GOK, M., SEFA, I.
 
Click to see author's profile on 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: 39 | Views: 53

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
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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Copyright ©2001-2017
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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