Fault Detection and Classification for Transmission Line Protection System Using Artificial Neural Network

Ei Phyo Thwe; Min Min Oo

doi:doi:10.11648/j.jeee.20160405.11

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Fault Detection and Classification for Transmission Line Protection System Using Artificial Neural Network

Ei Phyo Thwe, Min Min Oo

Published in Journal of Electrical and Electronic Engineering (Volume 4, Issue 5)

Received: 9 September 2016 Accepted: 23 September 2016 Published: 19 October 2016

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Abstract

The artificial neural network is a powerful tool for the detection of the transmission line faults due to its ability to differentiate between various patterns. This paper deals with the application of artificial neural networks (ANNs) to the fault detection and classification in high voltage transmission lines for high speed protection which can be used in digital power system protection. The three phase currents of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. The ANN was trained and tested using various sets of field data, which was obtained from the simulation of faults at various fault scenarios (fault types, fault locations and fault resistance) of 230 kV, 193.2 km in length “Mansan-Shwesaryan, Mandalay Region, Myanmar” transmission line using a computer program based on MATLAB/Simulink. Simulation results confirm that the proposed method can efficiently be used for accurate fault classification on the transmission line.

Published in	Journal of Electrical and Electronic Engineering (Volume 4, Issue 5)
DOI	10.11648/j.jeee.20160405.11
Page(s)	89-96
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), 2016. Published by Science Publishing Group

Keywords

Artificial Neural Network, Fault Detection, Classification, Transmission Line

References

[1]	Kim Chul-Hwan, Kim Hyun, Ko Young-Hun, Byun Sung-Hyun, Aggarwal Raj K, Johns Allan T. A novel fault-detection technique of high-impedance arcing faults in transmission lines using the wavelet transform. IEEE Trans Power Delivery 2002; 17 (4): 921–9.
[2]	Joe-Air Jiang, Ping-Lin Fan, Ching-Shan Chen, Chi-Shan Yu, Jin-Yi Sheu. A Fault Detection and Faulted Phase Selection Approach for Transmission Lines with Haar Wavelet Transform. In: Transmission and distribution conference and exposition 2003, vol. 3, IEEE PES, 7-12, September 2003. p. 285–9.
[3]	Liang Feng, Jeyasura B. Transmission line distance protection using wavelet transform algorithm. IEEE Trans Power Delivery 2004; 19 (2): 545–53.
[4]	Osman AH, Malik OP. Transmission line distance protection based on wavelet transforms. IEEE Trans Power Delivery 2004; 19 (2): 515–23.
[5]	Chanda D, Kishore NK, Sinha AK. A Wavelet multiresolution analysis for location of faults on transmission lines. Electrical Power Syst Res 2003; 25: 59–69.
[6]	Jafarian Peyman, Sanaye-Pasand Majid. A traveling wave-based protection technique using Wavelet/PCA analysis. IEEE Trans Power Delivery 2010; 25 (2): 588–99.
[7]	Martin Fransisco, Aguado Jose A. Wavelet-based ANN approach for transmission line protection. IEEE Trans Power Delivery 2003; 18 (4): 1572–4.
[8]	Tawfik MM, Marcos MM. ANN-based techniques for estimating fault location on transmission lines using prony method. IEEE Trans Power Delivery 2001; 16 (2): 219–24.
[9]	Silva KM, Souza BA, Brito NSD. Fault detection and classification in transmission lines based on wavelet transform and ANN. IEEE Trans Power Delivery 2006; 21 (4): 2058–63.
[10]	Phadke Arun G, Throp James S. Computer relaying for power systems. Research Study Press Ltd.; 1994.
[11]	Anamika Y, Yajnaseni D, “An Overview of Transmission Line Protection by Artificial Neural Network: Fault Detection, Fault Classification, Fault Location, and Fault Direction Discrimination,” vol. 2014, ID. 230382, 20 pages, 2014.
[12]	Majid Jamil, Sanjeev Kumar Sharma* and Rajveer Singh, “Fault detection and classification in electrical power transmission system using artificial neural network”, Jamil et al. Springer Plus (2015).
[13]	Subba Reddy. B, D. Sreenuvasulu Reddy, Dr. G. V. Marutheswar, “Identification of Fault Location in Multiple Transmission Lines by Wavelet Transform”, International Journal of Computational Engineering Research, vol. 4, issue. 2, 2014.
[14]	Qais Hashim Alsafasfeh, “Pattern Recognition for Fault Detection, Classification, and Localization in Electrical Power Systems”, Ph. D Thesis, Western Michigan University, 2010.
[15]	Huan, V. P., and Hung, L. K., “An ANFIS based approach to improve the fault location on 110 kV transmission line Dak Mil – Dak Nong”, IJCSI International Journal of Computer Science Issues, Vol. 11, Issue 3, No 1, 2014.
[16]	Vu Phan Huan, Le Kim Hung, Nguyen Hoang Viet, “Fault Classification and Location on 220 kV Transmission line Hoa Khanh – Hue Using Anfis Net,” Journal of Automation and Control Engineering, vol. 3, no. 2, April 2015.

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APA Style

Ei Phyo Thwe, Min Min Oo. (2016). Fault Detection and Classification for Transmission Line Protection System Using Artificial Neural Network. Journal of Electrical and Electronic Engineering, 4(5), 89-96. https://doi.org/10.11648/j.jeee.20160405.11

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ACS Style

Ei Phyo Thwe; Min Min Oo. Fault Detection and Classification for Transmission Line Protection System Using Artificial Neural Network. J. Electr. Electron. Eng. 2016, 4(5), 89-96. doi: 10.11648/j.jeee.20160405.11

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AMA Style

Ei Phyo Thwe, Min Min Oo. Fault Detection and Classification for Transmission Line Protection System Using Artificial Neural Network. J Electr Electron Eng. 2016;4(5):89-96. doi: 10.11648/j.jeee.20160405.11

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@article{10.11648/j.jeee.20160405.11,
  author = {Ei Phyo Thwe and Min Min Oo},
  title = {Fault Detection and Classification for Transmission Line Protection System Using Artificial Neural Network},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {4},
  number = {5},
  pages = {89-96},
  doi = {10.11648/j.jeee.20160405.11},
  url = {https://doi.org/10.11648/j.jeee.20160405.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20160405.11},
  abstract = {The artificial neural network is a powerful tool for the detection of the transmission line faults due to its ability to differentiate between various patterns. This paper deals with the application of artificial neural networks (ANNs) to the fault detection and classification in high voltage transmission lines for high speed protection which can be used in digital power system protection. The three phase currents of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. The ANN was trained and tested using various sets of field data, which was obtained from the simulation of faults at various fault scenarios (fault types, fault locations and fault resistance) of 230 kV, 193.2 km in length “Mansan-Shwesaryan, Mandalay Region, Myanmar” transmission line using a computer program based on MATLAB/Simulink. Simulation results confirm that the proposed method can efficiently be used for accurate fault classification on the transmission line.},
 year = {2016}
}

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TY  - JOUR
T1  - Fault Detection and Classification for Transmission Line Protection System Using Artificial Neural Network
AU  - Ei Phyo Thwe
AU  - Min Min Oo
Y1  - 2016/10/19
PY  - 2016
N1  - https://doi.org/10.11648/j.jeee.20160405.11
DO  - 10.11648/j.jeee.20160405.11
T2  - Journal of Electrical and Electronic Engineering
JF  - Journal of Electrical and Electronic Engineering
JO  - Journal of Electrical and Electronic Engineering
SP  - 89
EP  - 96
PB  - Science Publishing Group
SN  - 2329-1605
UR  - https://doi.org/10.11648/j.jeee.20160405.11
AB  - The artificial neural network is a powerful tool for the detection of the transmission line faults due to its ability to differentiate between various patterns. This paper deals with the application of artificial neural networks (ANNs) to the fault detection and classification in high voltage transmission lines for high speed protection which can be used in digital power system protection. The three phase currents of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. The ANN was trained and tested using various sets of field data, which was obtained from the simulation of faults at various fault scenarios (fault types, fault locations and fault resistance) of 230 kV, 193.2 km in length “Mansan-Shwesaryan, Mandalay Region, Myanmar” transmission line using a computer program based on MATLAB/Simulink. Simulation results confirm that the proposed method can efficiently be used for accurate fault classification on the transmission line.
VL  - 4
IS  - 5
ER  -

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Author Information

Ei Phyo Thwe

Dept. of Electrical Power Engineering, Mandalay Technological University, Mandalay, Myanmar
Min Min Oo

Dept. of Electrical Power Engineering, Mandalay Technological University, Mandalay, Myanmar

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Plain Text BibTeX RIS

APA Style

Ei Phyo Thwe, Min Min Oo. (2016). Fault Detection and Classification for Transmission Line Protection System Using Artificial Neural Network. Journal of Electrical and Electronic Engineering, 4(5), 89-96. https://doi.org/10.11648/j.jeee.20160405.11

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ACS Style

Ei Phyo Thwe; Min Min Oo. Fault Detection and Classification for Transmission Line Protection System Using Artificial Neural Network. J. Electr. Electron. Eng. 2016, 4(5), 89-96. doi: 10.11648/j.jeee.20160405.11

Copy | Download

AMA Style

Ei Phyo Thwe, Min Min Oo. Fault Detection and Classification for Transmission Line Protection System Using Artificial Neural Network. J Electr Electron Eng. 2016;4(5):89-96. doi: 10.11648/j.jeee.20160405.11

Copy | Download

@article{10.11648/j.jeee.20160405.11,
  author = {Ei Phyo Thwe and Min Min Oo},
  title = {Fault Detection and Classification for Transmission Line Protection System Using Artificial Neural Network},
  journal = {Journal of Electrical and Electronic Engineering},
  volume = {4},
  number = {5},
  pages = {89-96},
  doi = {10.11648/j.jeee.20160405.11},
  url = {https://doi.org/10.11648/j.jeee.20160405.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20160405.11},
  abstract = {The artificial neural network is a powerful tool for the detection of the transmission line faults due to its ability to differentiate between various patterns. This paper deals with the application of artificial neural networks (ANNs) to the fault detection and classification in high voltage transmission lines for high speed protection which can be used in digital power system protection. The three phase currents of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. The ANN was trained and tested using various sets of field data, which was obtained from the simulation of faults at various fault scenarios (fault types, fault locations and fault resistance) of 230 kV, 193.2 km in length “Mansan-Shwesaryan, Mandalay Region, Myanmar” transmission line using a computer program based on MATLAB/Simulink. Simulation results confirm that the proposed method can efficiently be used for accurate fault classification on the transmission line.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Fault Detection and Classification for Transmission Line Protection System Using Artificial Neural Network
AU  - Ei Phyo Thwe
AU  - Min Min Oo
Y1  - 2016/10/19
PY  - 2016
N1  - https://doi.org/10.11648/j.jeee.20160405.11
DO  - 10.11648/j.jeee.20160405.11
T2  - Journal of Electrical and Electronic Engineering
JF  - Journal of Electrical and Electronic Engineering
JO  - Journal of Electrical and Electronic Engineering
SP  - 89
EP  - 96
PB  - Science Publishing Group
SN  - 2329-1605
UR  - https://doi.org/10.11648/j.jeee.20160405.11
AB  - The artificial neural network is a powerful tool for the detection of the transmission line faults due to its ability to differentiate between various patterns. This paper deals with the application of artificial neural networks (ANNs) to the fault detection and classification in high voltage transmission lines for high speed protection which can be used in digital power system protection. The three phase currents of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. The ANN was trained and tested using various sets of field data, which was obtained from the simulation of faults at various fault scenarios (fault types, fault locations and fault resistance) of 230 kV, 193.2 km in length “Mansan-Shwesaryan, Mandalay Region, Myanmar” transmission line using a computer program based on MATLAB/Simulink. Simulation results confirm that the proposed method can efficiently be used for accurate fault classification on the transmission line.
VL  - 4
IS  - 5
ER  -

Copy | Download