American Journal of Electrical Power and Energy Systems

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Fault Detection and Classification Based on DWT and Modern Approaches for T.L Compensated with FACTS

Received: 29 October 2013    Accepted:     Published: 20 November 2013
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Abstract

A new approach for detecting and classifying a fault for transmission line compensated with Flexible AC Transmission System (FACTS) is presented in this paper. Unified Power Flow Controller (UPFC) is one of the most advanced FACTS devices that can simultaneously and independently control both the real and reactive power flow in a transmission line. The proposed technique consists of preprocessing module based on Discrete Wavelet Transform (DWT) in combination with Artificial Neural Network (ANN) or Gaussian Process (GP) for detecting and classifying fault events.

DOI 10.11648/j.epes.20130206.15
Published in American Journal of Electrical Power and Energy Systems (Volume 2, Issue 6, November 2013)
Page(s) 149-155
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), 2024. Published by Science Publishing Group

Keywords

Neural Network, Gaussian Process, Discrete Wavelet Transform, FACTS

References
[1] John J. Paserba, "How FACTS Controllers Benefit AC Transmission Systems", Transmission and Distribution Conference and Exposition, 2003 IEEE PES, Vol. 3, pp. 949-956, September 2003.
[2] John Wiley, "FACTS Modeling and Simulation in Power Networks", 2004.
[3] T. Manokaran, V.Karpagam, "Performance of Distance Relay Estimation in Transmission Line with UPFC", International Journal of Computer and Electrical Engineering, Vol. 2, No. 1, pp. 158-161, February 2010.
[4] Pavlos S. Georgilakis, Peter G. Vernados, "Flexible AC Transmission System Controllers", Materials Science Forum Vol. 670, pp 399-406, 2011.
[5] Lijun Cai, "Robust Coordinated Control of FACTS Devices in Large Power Systems", published by Logos Verlag Berlin 2004.
[6] Tomasz Okon and Kazimierz Wilkosz, "Influence of UPFC Device on Power System State Estimation", PowerTech IEEE, pp. 1-8, June 2011.
[7] Tomasz Okon, Kazimierz Wilkosz," Consideration of Different Operation Modes of UPFC in Power System State Estimation", Environment and Electrical Engineering (EEEIC) 10th International Conference, pp. 1-4, May 2011.
[8] Bo Hu, Kaigui Xie, Rajesh Karki, "Reliability Evaluation of Bulk Power Systems Incorporating UPFC", IEEE 2010.
[9] C. D. Schauder, L. Gyugyi, M. R. Lund, D. M. Hamai, T. R. Rietman, D. R. Torgerson, and A. Edris, "Operation of The Unified Power Flow Controller (UPFC) Under Practical Constrains", IEEE Transactions on Power Delivery, Vol. 13, No. 2, pp. 630-639, April 1998.
[10] Nampetch P., S.N. Singh, and Surapong C., "Modeling of UPFC and Its Parameters Selection", Power Electronics and Drive Systems 4th IEEE International Conference, Vol. 1, pp. 77-83, October 2011.
[11] Ali Ajami, S.H. Hosseini, and G.B. Gharehpetian, "Modelling and Controlling of UPFC for Power System Transient Studies", ECTI Transactions on Electrical Eng., Electronics, and Communications, Vol. 5, No. 2, pp. 29-35, August 2007.
[12] Mehrdad Ahmadi Kamarposhti, Mostafa Alinezhad, Hamid Lesani, Nemat Talebi, "Comparison of SVC, STATCOM, TCSC, and UPFC Controllers for Static Voltage Stability Evaluated by Continuation Power Flow Method", IEEE Electrical Power & Energy Conference, pp. 1-8, October 2008.
[13] P. K. Dash, A. K. Pradhan, G. Panda, A. C. Liew, "Digital Protection of Power Transmission Lines in The Presence of Series Connected FACTS Devices", IEEE, Vol. 3, pp. 1967-1972, January 2000.
[14] P.K. Dash, A.K. Pradhan, G. Panda, "Distance protection in the presence of unified power flow controller", Electric Power Systems Research 54, pp. 189–198, 2000.
[15] N.Zhang, M.Kezunovic, "Transmission Line Boundary Protection Using Wavelet Transform and Neural Network", IEEE Transactions on Power Delivery, Vol. 22, Issue 2, pp. 859-869, April 2007.
[16] Sriya Chakraborty, Shalini Singh, Anu Bhalla, Pallavi Saxena, Ramesh Padarla, "Wavelet Transform Based Fault Detection and Classification in Transmission Line", International Journal of Research in Engineering & Applied Sciences, ISSN: 2249-3905, Vol. 2, Issue 5, May 2012.
[17] Abdulhamid A. Abohagar, M.W.Mustafa, "Back Propagation Neural Network Aided Wavelet Transform for High Impedance Fault Detection and Faulty Phase Selection", IEEE International Conference on Power and Energy (PECon), pp. 790-795, December 2012.
[18] Francisco Martín, José A. Aguado, "Wavelet-Based ANN Approach for Transmission Line Protection", IEEE Transactions on Power Delivery, Vol. 18, No. 4, pp. 1572-1574, October 2003.
[19] Anant Oonsivilai, Sanom Saichoomdee, "Appliance of Recurrent Neural Network toward Distance Transmission Lines Protection", IEEE, pp. 1-4, January 2009.
[20] Janison R. de Carvalho, Denis V. Coury, Carlos A. Duque, David C. Jorge, "Development of Detection and Classification Stages for a New Distance Protection Approach Based on Cumulants and Neural Networks", Power and Energy Society General Meeting IEEE, pp. 1-7, July 2011.
[21] A.P.Vaidy, Prasad A. Venikar, "ANN Based Distance Protection of Long Transmission Lines by Considering the Effect of Fault Resistance", IEEE - International Conference On Advances In Engineering, Science And Management, pp. 590-594, March 2012.
[22] D.V Coury, D.C Jorge, "Artificial Neural Network Approach to Distance Protection of Transmission Lines", IEEE Transactions on Power Delivery, Vol. 13, No. 1, pp. 102-108, January 1998.
[23] E.A. Feilat and K. AI-Tallaq, "A New Approach For Distance Protection Using Artificial Neural Network", Universities Power Engineering Conference UPEC, Vol. 1, pp. 473-477, September 2004.
[24] Hannes Nickisch, Carl Edward Rasmussen, "Approximations for binary Gaussian process classification", Journal of Machine Learning Research 9, pp. 2035–2078, 2008.
[25] Pavle Boškoski, Matej Gašperin, Dejan Petelin, "Bearing fault prognostics based on signal complexity and Gaussian process models", IEEE conference, 2012.
[26] C. E. Rasmussen, C. K. I. Williams, "Gaussian Processes for Machine Learning", MIT Press, Cambridge 2006.
Author Information
  • Electrical Power & Machines Dept., Faculty of Engineering, Ain Shams University, Cairo, Egypt

  • Electrical Power & Machines Dept., Faculty of Engineering, Ain Shams University, Cairo, Egypt

  • Electrical Power & Machines Dept., Faculty of Engineering, Ain Shams University, Cairo, Egypt

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  • APA Style

    Noha Mahmoud Bastawy, Hossam El-din Talaat, Amr Mohamed Ibrahim. (2013). Fault Detection and Classification Based on DWT and Modern Approaches for T.L Compensated with FACTS. American Journal of Electrical Power and Energy Systems, 2(6), 149-155. https://doi.org/10.11648/j.epes.20130206.15

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    ACS Style

    Noha Mahmoud Bastawy; Hossam El-din Talaat; Amr Mohamed Ibrahim. Fault Detection and Classification Based on DWT and Modern Approaches for T.L Compensated with FACTS. Am. J. Electr. Power Energy Syst. 2013, 2(6), 149-155. doi: 10.11648/j.epes.20130206.15

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    AMA Style

    Noha Mahmoud Bastawy, Hossam El-din Talaat, Amr Mohamed Ibrahim. Fault Detection and Classification Based on DWT and Modern Approaches for T.L Compensated with FACTS. Am J Electr Power Energy Syst. 2013;2(6):149-155. doi: 10.11648/j.epes.20130206.15

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  • @article{10.11648/j.epes.20130206.15,
      author = {Noha Mahmoud Bastawy and Hossam El-din Talaat and Amr Mohamed Ibrahim},
      title = {Fault Detection and Classification Based on DWT and Modern Approaches for T.L Compensated with FACTS},
      journal = {American Journal of Electrical Power and Energy Systems},
      volume = {2},
      number = {6},
      pages = {149-155},
      doi = {10.11648/j.epes.20130206.15},
      url = {https://doi.org/10.11648/j.epes.20130206.15},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.epes.20130206.15},
      abstract = {A new approach for detecting and classifying a fault for transmission line compensated with Flexible AC Transmission System (FACTS) is presented in this paper. Unified Power Flow Controller (UPFC) is one of the most advanced FACTS devices that can simultaneously and independently control both the real and reactive power flow in a transmission line. The proposed technique consists of preprocessing module based on Discrete Wavelet Transform (DWT) in combination with Artificial Neural Network (ANN) or Gaussian Process (GP) for detecting and classifying fault events.},
     year = {2013}
    }
    

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    T1  - Fault Detection and Classification Based on DWT and Modern Approaches for T.L Compensated with FACTS
    AU  - Noha Mahmoud Bastawy
    AU  - Hossam El-din Talaat
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    JF  - American Journal of Electrical Power and Energy Systems
    JO  - American Journal of Electrical Power and Energy Systems
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    UR  - https://doi.org/10.11648/j.epes.20130206.15
    AB  - A new approach for detecting and classifying a fault for transmission line compensated with Flexible AC Transmission System (FACTS) is presented in this paper. Unified Power Flow Controller (UPFC) is one of the most advanced FACTS devices that can simultaneously and independently control both the real and reactive power flow in a transmission line. The proposed technique consists of preprocessing module based on Discrete Wavelet Transform (DWT) in combination with Artificial Neural Network (ANN) or Gaussian Process (GP) for detecting and classifying fault events.
    VL  - 2
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    ER  - 

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