Fault Location in the Transmission Network Using a Discrete Wavelet Transform

Majid Dashtdar; Masoud Dashtdar

doi:doi:10.11648/j.ajece.20190301.14

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Fault Location in the Transmission Network Using a Discrete Wavelet Transform

Majid Dashtdar, Masoud Dashtdar

Published in American Journal of Electrical and Computer Engineering (Volume 3, Issue 1)

Received: 19 March 2019 Accepted: 17 April 2019 Published: 25 June 2019

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Abstract

In this paper, a discrete wavelet transform (DWT) has been utilized for processing the current signal in order to fault-location evaluation in network transmission using pre-fault and post-fault current data of both the terminals of a transmission line. In fact, the basis of the work is based on the information recorded before the fault at the end of the line and after the fault at the beginning of the line received by the relay. Obviously, high-frequency components are created at the time of the fault, which is a way of extracting these components using a wavelet transform. In this design, characteristics extorted from synchronous recording of three-phase current signals at the two terminals using DWT. In the following, can accurately estimate the exact location of the fault in the transmission network by extraction and subtracting of the minimum and maximum components of the DWT approximate and detail components of the signal before and after the fault (pre-fault and post-fault).The simulation results reveal that the minimum and maximum extracted components are highly dependent on the fault resistance. Hence, due to increase the fault resistance, the level of signal decomposition has to be increased so that the algorithm is not compromised. Eventually, the proposed method is tested on the transmission network of 735 kV at different distances of the transmission line, which indicates that the proposed algorithm can accurately estimate the fault distance, depending on the type of fault (including low-impedance and high-impedance fault) by changing the signal decomposition level.

Published in	American Journal of Electrical and Computer Engineering (Volume 3, Issue 1)
DOI	10.11648/j.ajece.20190301.14
Page(s)	30-37
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Fault Location, High-Frequency Components, Signal Decomposition, Wavelet Transform

References

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[2]	Dashtdar, Masoud, Rahman Dashti, and Hamid Reza Shaker. "Distribution network fault section identification and fault location using artificial neural network." 2018 5th International Conference on Electrical and Electronic Engineering (ICEEE). IEEE, 2018.
[3]	Dashtdar, Masoud. "Fault Location in Distribution Network Based on Fault Current Analysis Using Artificial Neural Network." Journal of Electrical & Computer Engineering 1.2 (2018): 18-32.
[4]	Chiradeja, Pathomthat, and Atthapol Ngaopitakkul. "Classification of Lightning and Faults in Transmission Line Systems Using Discrete Wavelet Transform." Mathematical Problems in Engineering 2018 (2018).
[5]	Lopes FV, Kusel BF, Silva KM., "Traveling Wave-Based Fault Location on Half-Wavelength Transmission Lines", IEEE Latin America Transactions 14.1 (2016).
[6]	Bo ZQ, Weller G, Redfern MA, "Accurate fault location technique for distribution system using fault-generated high-frequency transient voltage signals", Generation, Transmission and Distribution, IEE Proceedings-. Vol.146. No. 1. IET, 1999.
[7]	Rao A, Bogale B, "Accurate Fault Location Technique on Power Transmission Lines with use of Phasor Measurements", International Journal of Engineering Research and Technology. Vol. 4. No. 02 (February-2015). ESRSA Publications, 2015.
[8]	Xu Z, Zhang Z, "What accuracy can we expect from the single-ended fault locator?", Protective Relay Engineers,2015 68th Annual Conference for. IEEE, 2015.
[9]	Venugopal M, Tiwari C, "A novel algorithm to determine fault location in a transmission line using PMU measurements", Smart Instrumentation, Measurement and Applications (ICSIMA), 2013 IEEE International Conference on. IEEE, 2013.
[10]	Elkalashy NI, Kawady TA, Khater WM, Taalab AM," Unsynchronized Fault-Location Technique for Double-Circuit Transmission Systems Independent of Line Parameters", IEEE Transactions on Power Delivery.2015.
[11]	Kapoor, Gaurav. "A Fault-location Evaluation Method of a 330 kV Three-Phase Transmission Line by Using Discrete Wavelet Transform." International Journal of Engineering Design & Analysis 1.1 (2018): 5-10.
[12]	Magnago, F. H. and Abur, A., "Fault Location Using Wavelet", IEEE Trans. on Power Delivery, Vol. 13, No.4, PP. 1475-1480, October 1998.
[13]	Chen, Yann Qi, Olga Fink, and Giovanni Sansavini. "Combined fault location and classification for power transmission lines fault diagnosis with integrated feature extraction." IEEE Transactions on Industrial Electronics 65.1 (2018): 561-569.
[14]	Wang, Mei, Changfeng Xu, and Huimin Lu. "Fault Location Without Wave Velocity Influence Using Wavelet and Clark Transform." Artificial Intelligence and Robotics. Springer, Cham, 2018. 321-326.
[15]	Abraham, Sherura. "IMPROVING FAULT LOCATION OF THE ARC REFLECTION METHOD USING THE CONTINUOUS WAVELET TRANSFORM." (2018).
[16]	Sarkar, Animesh, and Bikash Patel. "RBF Neural Network-Based Wavelet Packet Energy-Aided Fault Localization on a Hybrid Transmission Line." Advances in Communication, Devices and Networking. Springer, Singapore, 2018. 807-815.
[17]	Saini, Makmur, et al. "Algorithm for Fault Location and Classification on Parallel Transmission Line using Wavelet based on Clarke’s Transform." International Journal of Electrical and Computer Engineering (IJECE) 8.2 (2018): 699-710.
[18]	Dashtdar, Majid, Masoud Dashtdar. "Fault Location in the Transmission Network Based on the Analysis of the Recorded Current by the Relay Using a Discrete Wavelet Transform." (2019).

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APA Style

Majid Dashtdar, Masoud Dashtdar. (2019). Fault Location in the Transmission Network Using a Discrete Wavelet Transform. American Journal of Electrical and Computer Engineering, 3(1), 30-37. https://doi.org/10.11648/j.ajece.20190301.14

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ACS Style

Majid Dashtdar; Masoud Dashtdar. Fault Location in the Transmission Network Using a Discrete Wavelet Transform. Am. J. Electr. Comput. Eng. 2019, 3(1), 30-37. doi: 10.11648/j.ajece.20190301.14

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AMA Style

Majid Dashtdar, Masoud Dashtdar. Fault Location in the Transmission Network Using a Discrete Wavelet Transform. Am J Electr Comput Eng. 2019;3(1):30-37. doi: 10.11648/j.ajece.20190301.14

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@article{10.11648/j.ajece.20190301.14,
  author = {Majid Dashtdar and Masoud Dashtdar},
  title = {Fault Location in the Transmission Network Using a Discrete Wavelet Transform},
  journal = {American Journal of Electrical and Computer Engineering},
  volume = {3},
  number = {1},
  pages = {30-37},
  doi = {10.11648/j.ajece.20190301.14},
  url = {https://doi.org/10.11648/j.ajece.20190301.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajece.20190301.14},
  abstract = {In this paper, a discrete wavelet transform (DWT) has been utilized for processing the current signal in order to fault-location evaluation in network transmission using pre-fault and post-fault current data of both the terminals of a transmission line. In fact, the basis of the work is based on the information recorded before the fault at the end of the line and after the fault at the beginning of the line received by the relay. Obviously, high-frequency components are created at the time of the fault, which is a way of extracting these components using a wavelet transform. In this design, characteristics extorted from synchronous recording of three-phase current signals at the two terminals using DWT. In the following, can accurately estimate the exact location of the fault in the transmission network by extraction and subtracting of the minimum and maximum components of the DWT approximate and detail components of the signal before and after the fault (pre-fault and post-fault).The simulation results reveal that the minimum and maximum extracted components are highly dependent on the fault resistance. Hence, due to increase the fault resistance, the level of signal decomposition has to be increased so that the algorithm is not compromised. Eventually, the proposed method is tested on the transmission network of 735 kV at different distances of the transmission line, which indicates that the proposed algorithm can accurately estimate the fault distance, depending on the type of fault (including low-impedance and high-impedance fault) by changing the signal decomposition level.},
 year = {2019}
}

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TY  - JOUR
T1  - Fault Location in the Transmission Network Using a Discrete Wavelet Transform
AU  - Majid Dashtdar
AU  - Masoud Dashtdar
Y1  - 2019/06/25
PY  - 2019
N1  - https://doi.org/10.11648/j.ajece.20190301.14
DO  - 10.11648/j.ajece.20190301.14
T2  - American Journal of Electrical and Computer Engineering
JF  - American Journal of Electrical and Computer Engineering
JO  - American Journal of Electrical and Computer Engineering
SP  - 30
EP  - 37
PB  - Science Publishing Group
SN  - 2640-0502
UR  - https://doi.org/10.11648/j.ajece.20190301.14
AB  - In this paper, a discrete wavelet transform (DWT) has been utilized for processing the current signal in order to fault-location evaluation in network transmission using pre-fault and post-fault current data of both the terminals of a transmission line. In fact, the basis of the work is based on the information recorded before the fault at the end of the line and after the fault at the beginning of the line received by the relay. Obviously, high-frequency components are created at the time of the fault, which is a way of extracting these components using a wavelet transform. In this design, characteristics extorted from synchronous recording of three-phase current signals at the two terminals using DWT. In the following, can accurately estimate the exact location of the fault in the transmission network by extraction and subtracting of the minimum and maximum components of the DWT approximate and detail components of the signal before and after the fault (pre-fault and post-fault).The simulation results reveal that the minimum and maximum extracted components are highly dependent on the fault resistance. Hence, due to increase the fault resistance, the level of signal decomposition has to be increased so that the algorithm is not compromised. Eventually, the proposed method is tested on the transmission network of 735 kV at different distances of the transmission line, which indicates that the proposed algorithm can accurately estimate the fault distance, depending on the type of fault (including low-impedance and high-impedance fault) by changing the signal decomposition level.
VL  - 3
IS  - 1
ER  -

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Author Information

Majid Dashtdar

Electrical Engineering Department, Bushehr Branch, Islamic Azad University, Bushehr, Iran
Masoud Dashtdar

Electrical Engineering Department, Bushehr Branch, Islamic Azad University, Bushehr, Iran

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Plain Text BibTeX RIS

APA Style

Majid Dashtdar, Masoud Dashtdar. (2019). Fault Location in the Transmission Network Using a Discrete Wavelet Transform. American Journal of Electrical and Computer Engineering, 3(1), 30-37. https://doi.org/10.11648/j.ajece.20190301.14

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ACS Style

Majid Dashtdar; Masoud Dashtdar. Fault Location in the Transmission Network Using a Discrete Wavelet Transform. Am. J. Electr. Comput. Eng. 2019, 3(1), 30-37. doi: 10.11648/j.ajece.20190301.14

Copy | Download

AMA Style

Majid Dashtdar, Masoud Dashtdar. Fault Location in the Transmission Network Using a Discrete Wavelet Transform. Am J Electr Comput Eng. 2019;3(1):30-37. doi: 10.11648/j.ajece.20190301.14

Copy | Download

@article{10.11648/j.ajece.20190301.14,
  author = {Majid Dashtdar and Masoud Dashtdar},
  title = {Fault Location in the Transmission Network Using a Discrete Wavelet Transform},
  journal = {American Journal of Electrical and Computer Engineering},
  volume = {3},
  number = {1},
  pages = {30-37},
  doi = {10.11648/j.ajece.20190301.14},
  url = {https://doi.org/10.11648/j.ajece.20190301.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajece.20190301.14},
  abstract = {In this paper, a discrete wavelet transform (DWT) has been utilized for processing the current signal in order to fault-location evaluation in network transmission using pre-fault and post-fault current data of both the terminals of a transmission line. In fact, the basis of the work is based on the information recorded before the fault at the end of the line and after the fault at the beginning of the line received by the relay. Obviously, high-frequency components are created at the time of the fault, which is a way of extracting these components using a wavelet transform. In this design, characteristics extorted from synchronous recording of three-phase current signals at the two terminals using DWT. In the following, can accurately estimate the exact location of the fault in the transmission network by extraction and subtracting of the minimum and maximum components of the DWT approximate and detail components of the signal before and after the fault (pre-fault and post-fault).The simulation results reveal that the minimum and maximum extracted components are highly dependent on the fault resistance. Hence, due to increase the fault resistance, the level of signal decomposition has to be increased so that the algorithm is not compromised. Eventually, the proposed method is tested on the transmission network of 735 kV at different distances of the transmission line, which indicates that the proposed algorithm can accurately estimate the fault distance, depending on the type of fault (including low-impedance and high-impedance fault) by changing the signal decomposition level.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Fault Location in the Transmission Network Using a Discrete Wavelet Transform
AU  - Majid Dashtdar
AU  - Masoud Dashtdar
Y1  - 2019/06/25
PY  - 2019
N1  - https://doi.org/10.11648/j.ajece.20190301.14
DO  - 10.11648/j.ajece.20190301.14
T2  - American Journal of Electrical and Computer Engineering
JF  - American Journal of Electrical and Computer Engineering
JO  - American Journal of Electrical and Computer Engineering
SP  - 30
EP  - 37
PB  - Science Publishing Group
SN  - 2640-0502
UR  - https://doi.org/10.11648/j.ajece.20190301.14
AB  - In this paper, a discrete wavelet transform (DWT) has been utilized for processing the current signal in order to fault-location evaluation in network transmission using pre-fault and post-fault current data of both the terminals of a transmission line. In fact, the basis of the work is based on the information recorded before the fault at the end of the line and after the fault at the beginning of the line received by the relay. Obviously, high-frequency components are created at the time of the fault, which is a way of extracting these components using a wavelet transform. In this design, characteristics extorted from synchronous recording of three-phase current signals at the two terminals using DWT. In the following, can accurately estimate the exact location of the fault in the transmission network by extraction and subtracting of the minimum and maximum components of the DWT approximate and detail components of the signal before and after the fault (pre-fault and post-fault).The simulation results reveal that the minimum and maximum extracted components are highly dependent on the fault resistance. Hence, due to increase the fault resistance, the level of signal decomposition has to be increased so that the algorithm is not compromised. Eventually, the proposed method is tested on the transmission network of 735 kV at different distances of the transmission line, which indicates that the proposed algorithm can accurately estimate the fault distance, depending on the type of fault (including low-impedance and high-impedance fault) by changing the signal decomposition level.
VL  - 3
IS  - 1
ER  -

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