Home A New Hybrid Protection Algorithm for Protection of Power Transformer Based on Discrete Wavelet Transform and ANFIS Inference Systems
Article
Licensed
Unlicensed Requires Authentication

A New Hybrid Protection Algorithm for Protection of Power Transformer Based on Discrete Wavelet Transform and ANFIS Inference Systems

  • A.M. Salama , K. M. Abdel-Latif , Mohamed M. Ismail EMAIL logo and S. M. Mousa
Published/Copyright: April 25, 2018

Abstract

This paper presents a new protection algorithm for power transformers. The new algorithm is based on Discrete Wavelet Transform (DWT) and (ANFIS) Inference System. The simulation of power transformer is done using BCTRAN subroutine of ATP software to simulate the internal faults cases. The protection algorithm using DWT and ANFIS is implemented MATLAB Simulink software. The new Algorithm is tested on 40/40/15 MVA, 220/70/11.5 KV power transformer. The proposed algorithm satisfies high degree of accuracy and faster response time.

References

[1] Nylen R, Power Transformer Protection Application Guide, ABB Relays AB, 5-721 71 Västerås, Sweden, Marcha 1988.Search in Google Scholar

[2] Blackburn LJ. Protective relaying: principles and applications. New York: Marcel Dekker Inc., 1986.Search in Google Scholar

[3] Anderson PM. Power system protection. New York: IEEE, 1999.10.1109/9780470545591Search in Google Scholar

[4] Guzman A, Benmouyal G, Aluve H. A current-based solution for transformer differential protection. IEEE Trans Power Delivery. 2001;16(4):485–491.10.1109/61.956726Search in Google Scholar

[5] Vishwakarma D, Balaga H, Nath H. Application of genetic algorithm trained master slave neural network for differential protection of power transformer. 9th International Conference on Computer Engineering & Systems (ICCES), pp. 164–9, 22–23 Dec 2014.10.1109/ICCES.2014.7030950Search in Google Scholar

[6] Tripathy M, Maheshwari RP, Verma HK. Power transformer differential protection based on optimal probabilistic neural network. IEEE Trans Power Del. 2010 Jan;25(1):102–12.10.1109/TPWRD.2009.2028800Search in Google Scholar

[7] Samantray SR, Dash PI. Decision tree based discrimination between inrush currents and internal faults in power transformer. Electr Power Energy Syst. 2011;33:1043–8.10.1016/j.ijepes.2011.01.021Search in Google Scholar

[8] Zhou B, Cao C, Li C, Cao Y, Chen C, Li Y, et al. Hybrid islanding detection method based on decision tree and positive feedback for distributed generations. IET Gener Transm Distrib. 2015 November;9(14):1819–25.10.1049/iet-gtd.2015.0069Search in Google Scholar

[9] Ghanbari T, Samet H, Ghafourifard J. New approach to improve sensitivity of differential and restricted earth fault protections for industrial transformers. IET Gener Transm Distrib. 2016 April;10(6):1486–94.10.1049/iet-gtd.2015.1343Search in Google Scholar

[10] Megahed AI, Ramadan A, ElMahdy W. Power transformer differential relay using wavelet transform energies. Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the twenty-first Century, 2008 IEEE, pp. 1–6.10.1109/PES.2008.4596000Search in Google Scholar

[11] Shah AM, Bhalja BR. Discrimination between internal faults and other disturbances in transformer using the support vector machine. IEEE Trans Power Del. 2013 July;28(3):1508–15.10.1109/TPWRD.2012.2227979Search in Google Scholar

[12] Medeiros R, Costa F, Fernandes J. Differential protection of power transformers using the wavelet transform. IEEE in PES General Meeting Conference & Exposition, Natinal Harbor, MD, pp.1–5, 27–31 July 2014.10.1109/PESGM.2014.6939837Search in Google Scholar

[13] Jang JSR. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern. 1993;23(3):665–85.10.1109/21.256541Search in Google Scholar

[14] Jang JSR, Sun CT, Mizutani E. Neuro-fuzzy and soft computing. Englewood Cliffs, NJ: Prentice Hall, 1997Search in Google Scholar

[15] Gülera İ, Übeylib ED. Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. J Neurosci Methods. 2005;148(2):113–21.10.1016/j.jneumeth.2005.04.013Search in Google Scholar PubMed

[16] Al-Hmouz A, Shen J, Al-Hmouz R, Yan J. Modeling and simulation of an adaptive neuro-fuzzy inference system (ANFIS) for mobile learning. IEEE Trans Learn Technol. 2012;5(3):226–3710.1109/TLT.2011.36Search in Google Scholar

[17] Bastard P, Bertrand P, Meunier M. A Transformer Model for Winding Fault Studies. IEEE Trans Power Deliv. 1994;9(2):690–9.10.1109/61.296246Search in Google Scholar

[18] Oliveira MO, Bretas AS. Application of discrete wavelet transform for differential protection of power transformers. New York: IEEE, 2009: 349–66.10.1109/PTC.2009.5282195Search in Google Scholar

[19] Wang J, Gao XZ, Tanskanen JMA. Epileptic EEG signal classification with ANFIS based on harmony search method. 2012 Eighth International Conference on Computational Intelligence and Security (CIS), 17–18 Nov 2012.10.1109/CIS.2012.159Search in Google Scholar

[20] Rakhshan M, Shabaninia F, Shasadeghi M. ANFIS approach for tracking control of MEMS triaxial gyroscope. Mseee J. 2014;1:35–40Search in Google Scholar

[21] Advances in computational intelligence. 13th International Work-Conference on Artificial Neural Networks, IWANN 2015, Palma de Mallorca, Spain, Proceedings, Part II 10–12 June 2015, Springer Science.Search in Google Scholar

Received: 2017-11-19
Accepted: 2018-4-12
Published Online: 2018-4-25

© 2018 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 28.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/ijeeps-2017-0248/html
Scroll to top button