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

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


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Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

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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.

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  3/2010 - 19
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Approaches in High Impedance Fault Detection - A Chronological Review

SEDIGHIZADEH, M. See more information about SEDIGHIZADEH, M. on SCOPUS See more information about SEDIGHIZADEH, M. on IEEExplore See more information about SEDIGHIZADEH, M. on Web of Science, REZAZADEH, A. See more information about  REZAZADEH, A. on SCOPUS See more information about  REZAZADEH, A. on SCOPUS See more information about REZAZADEH, A. on Web of Science, ELKALASHY, N. I. See more information about ELKALASHY, N. I. on SCOPUS See more information about ELKALASHY, N. I. on SCOPUS See more information about ELKALASHY, N. 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 (1,146 KB) | Citation | Downloads: 1,792 | Views: 1,046

Author keywords
chronological, classic, detection, high impedance fault, heuristic

References keywords
No relevant keywords could be extracted from the references.

About this article
Date of Publication: 2010-08-31
Volume 10, Issue 3, Year 2010, On page(s): 114 - 128
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.03019
Web of Science Accession Number: 000281805600019
SCOPUS ID: 77956625970

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This paper reviews the major contributions to the high impedance fault (HIF) detection field throughout a 48-year period, from 1960 up to 2008, from classic approaches to heuristic algorithms. After surveying around 225 papers in the field, the amount of existing works for each method is identified and classified. The paper concludes with comparative tables and graphs demonstrating the frequency of each high impedance fault detection methods, and so it can be used as a guideline for researchers in this field.

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[1] Detection of High Impedance Fault in Distribution Networks, Kavaskar, Sekar, Mohanty, Nalin Kant, Ain Shams Engineering Journal, ISSN 2090-4479, 2018.
Digital Object Identifier: 10.1016/j.asej.2018.04.006

[2] Multicycle Incipient Fault Detection and Location for Medium Voltage Underground Cable, Zhang, Wenhai, Xiao, Xianyong, Zhou, Kai, Xu, Wilsun, Jing, Yindi, IEEE Transactions on Power Delivery, ISSN 0885-8977, Issue 3, Volume 32, 2017.
Digital Object Identifier: 10.1109/TPWRD.2016.2615886

[3] Voltage Based Detection Method for High Impedance Fault in a Distribution System, Thomas, Mini Shaji, Bhaskar, Namrata, Prakash, Anupama, Journal of The Institution of Engineers (India): Series B, ISSN 2250-2106, Issue 3, Volume 97, 2016.
Digital Object Identifier: 10.1007/s40031-015-0203-7

[4] High impedance fault detection in power distribution systems using wavelet transform and evolving neural network, Silva, Sergio, Costa, Pyramo, Gouvea, Maury, Lacerda, Alcyr, Alves, Franciele, Leite, Daniel, Electric Power Systems Research, ISSN 0378-7796, Issue , 2018.
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[5] Approach for identification and classification of HIFs in medium voltage distribution networks, Hubana, Tarik, Saric, Mirza, Avdaković, Samir, IET Generation, Transmission & Distribution, ISSN 1751-8687, Issue 5, Volume 12, 2018.
Digital Object Identifier: 10.1049/iet-gtd.2017.0883

[6] A comprehensive review on DC arc faults and their diagnosis methods in photovoltaic systems, Lu, Shibo, Phung, B.T., Zhang, Daming, Renewable and Sustainable Energy Reviews, ISSN 1364-0321, Issue , 2018.
Digital Object Identifier: 10.1016/j.rser.2018.03.010

[7] Fault Detection and Localization in Transmission Lines with a Static Synchronous Series Compensator, REYES-ARCHUNDIA, E., GUARDADO, J. L., MORENO-GOYTIA, E. L., GUTIERREZ-GNECCHI, J. A., MARTINEZ-CARDENAS, F., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 3, Volume 15, 2015.
Digital Object Identifier: 10.4316/AECE.2015.03003
[CrossRef] [Full text]

[8] Evolving neuro-fuzzy network for real-time high impedance fault detection and classification, Silva, Sergio, Costa, Pyramo, Santana, Marcio, Leite, Daniel, Neural Computing and Applications, ISSN 0941-0643, 2018.
Digital Object Identifier: 10.1007/s00521-018-3789-2

[9] Method for high-impedance fault detection, Laaksonen, Hannu, Hovila, Petri, CIRED - Open Access Proceedings Journal, ISSN 2515-0855, Issue 1, Volume 2017, 2017.
Digital Object Identifier: 10.1049/oap-cired.2017.0308

[10] Combined Mathematical Morphology and Data Mining Based High Impedance Fault Detection, Sekar, Kavaskar, Mohanty, Nalin Kant, Energy Procedia, ISSN 1876-6102, Issue , 2017.
Digital Object Identifier: 10.1016/j.egypro.2017.05.161

[11] Non-linear high impedance fault distance estimation in power distribution systems: A continually online-trained neural network approach, Farias, Patrick E., de Morais, Adriano Peres, Rossini, Jean Pereira, Cardoso, Ghendy, Electric Power Systems Research, ISSN 0378-7796, Issue , 2018.
Digital Object Identifier: 10.1016/j.epsr.2017.11.018

[12] Model-Based General Arcing Fault Detection in Medium-Voltage Distribution Lines, Zhang, Wenhai, Jing, Yindi, Xiao, Xianyong, IEEE Transactions on Power Delivery, ISSN 0885-8977, Issue 5, Volume 31, 2016.
Digital Object Identifier: 10.1109/TPWRD.2016.2518738

[13] Classification of Many Abnormal Events in Radial Distribution Feeders Using the Complex Morlet Wavelet and Decision Trees, Almalki, Mishari, Hatziadoniu, Constantine, Energies, ISSN 1996-1073, Issue 3, Volume 11, 2018.
Digital Object Identifier: 10.3390/en11030546

[14] Detection of High Impedance Fault in Power Distribution Systems Using Mathematical Morphology, Gautam, Suresh, Brahma,, IEEE Transactions on Power Systems, ISSN 0885-8950, Issue 2, Volume 28, 2013.
Digital Object Identifier: 10.1109/TPWRS.2012.2215630

[15] High-Impedance Fault Detection in the Distribution Network Using the Time-Frequency-Based Algorithm, Ghaderi, Amin, Mohammadpour, Hossein Ali, Ginn, Herbert L., Shin, Yong-June, IEEE Transactions on Power Delivery, ISSN 0885-8977, Issue 3, Volume 30, 2015.
Digital Object Identifier: 10.1109/TPWRD.2014.2361207

[16] Analysis of High-Frequency Impedance Measurement Techniques for Power Line Network Sensing, Passerini, Federico, Tonello, Andrea M., IEEE Sensors Journal, ISSN 1530-437X, Issue 23, Volume 17, 2017.
Digital Object Identifier: 10.1109/JSEN.2017.2732737

[17] High impedance fault detection method efficiency: Simulation vs. real-world data acquisition, Ghaderi, Amin, Mohammadpour, Hossein Ali, Ginn, Herbert, 2015 IEEE Power and Energy Conference at Illinois (PECI), ISBN 978-1-4799-7949-3, 2015.
Digital Object Identifier: 10.1109/PECI.2015.7064882

[18] The arcing fault based multi-cycle incipient fault detection for underground cable, Zhang, Wenhai, Tang, Tuhua, Yin, Xinglu, Qu, Guanglong, Wang, Yang, 2016 China International Conference on Electricity Distribution (CICED), ISBN 978-1-4673-9070-5, 2016.
Digital Object Identifier: 10.1109/CICED.2016.7576142

[19] High impedance arcing fault detection in MV networks using discrete wavelet transform and Artificial Neural Networks, Vijayachandran, Gayathri, Mathew, Bobin.K., 2012 International Conference on Green Technologies (ICGT), ISBN 978-1-4673-2636-0, 2012.
Digital Object Identifier: 10.1109/ICGT.2012.6477953

[20] High impedance fault detection technique based on Discrete Wavelet Transform and support vector machine in power distribution networks, Moloi, K., Jordaan, J. A., Hamam, Y., 2017 IEEE AFRICON, ISBN 978-1-5386-2775-4, 2017.
Digital Object Identifier: 10.1109/AFRCON.2017.8095447

[21] Mathematical morphology-based fault detection technique for power distribution systems subjected to resonant grounding, Barik, M. A., Gargoom, A., Mahmud, M. A., Haque, M. E., Oo, Amanullah M. T., Al-Khalidi, Hassan, 2017 IEEE Power & Energy Society General Meeting, ISBN 978-1-5386-2212-4, 2017.
Digital Object Identifier: 10.1109/PESGM.2017.8274459

[22] Enhanced MV microgrid protection scheme for detecting high-impedance faults, Laaksonen, Hannu, Hovila, Petri, 2017 IEEE Manchester PowerTech, ISBN 978-1-5090-4237-1, 2017.
Digital Object Identifier: 10.1109/PTC.2017.7980899

[23] A novel high impedance fault detection technique in distribution systems with distributed generators, Nayak, Paresh Kumar, Sarwagya, Kumari, Biswal, Tapaswini, 2016 National Power Systems Conference (NPSC), ISBN 978-1-4673-9968-5, 2016.
Digital Object Identifier: 10.1109/NPSC.2016.7858855

[24] High Impedance Fault Classification Using Wavelet Transform and Artificial Neural Network, Kannan, A. Nirmal, Rathinam, A., 2012 Fourth International Conference on Computational Intelligence and Communication Networks, ISBN 978-0-7695-4850-0, 2012.
Digital Object Identifier: 10.1109/CICN.2012.122

[25] Power line fault detection and localization using high frequency impedance measurement, Passerini, Federico, Tonello, Andrea M., 2017 IEEE International Symposium on Power Line Communications and its Applications (ISPLC), ISBN 978-1-5090-2389-9, 2017.
Digital Object Identifier: 10.1109/ISPLC.2017.7897102

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania

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