Neural Network Based Transformer Differential Protection

NEURAL NETWORK BASED TRANSFORMER DIFFERENTIAL PROTECTION
Abstract: The role of a differential relay for power transformer is to trip during fault condition and blocks the tripping during inrush, overexcitation, CT saturation conditions of the power transformer. Conventional harmonic restrained relay may mal operate due to the presence of the second and fifth harmonic during internal faults because of non-linear loads and capacitance in the transmission lines. This project presents a technique for classifying transient phenomena in power transformers for differential protection. Discrimination among different operating conditions (i.e., normal, inrush, overexcitation, CT saturation) and internal faults of the power transformer is achieved by differential relay using the algorithm based on wavelet transform with Neural Network. The wavelet transform is a powerful tool in the analysis of the power transformer transient phenomena because of its ability to extract information from the transient signals simultaneously in both the time and frequency domain. Neural network is used because of its self-learning and highly nonlinear mapping capability. Keywords – Wavelet Transform, back propagation neural netwok, transformer differential protection. I.Introduction Differential protection is the primary protection for larger power transformers. It contains the operation by all types of internal faults, and blocks the operations of the transformer by inrush, overexcitation and external faults. Since a magnetizing inrush current generally contains a large second harmonic component in comparison to an internal fault, conventional transformer protection systems are designed to restrain during inrush transient phenomenon by sensing this large second harmonic. However, the second harmonic component may also be generated during internal faults in the power transformer, due to CT saturation or the presence of a shunt capacitor or the distributive capacitance in a long EHV

References: [1] Fahrudin Mekic, Ramsis Girgis, Zoran Gajic, Ed teNyenhuis, “Power Transformer Characteristics and Their Effect on Protective Relays”, 33rd Western Protective Relay Conference, October 17-19, 2006 [2] P. Bastard, M. Meunier, and H. Regal, "Neural-network-based algorithm for power transformer differential relays," IEE Proceedings on Generation, Transmission and Distribution, vol. 142, no. 4, pp. 386-392, July 1995. [3] G. Perez, A. J. Flechsig, J. L. Meador, and Z. Obradovic, "Training an artificial neural network to discriminate between magnetizing inrush and internal fault," IEEE Transactions on Power Delivery, vol. 9, no. 1, pp.434-441, January 1994. [4] P. B. Grcar and D. Dolinar, "Improved operation of power transformer protection using artificial neural network," IEEE Transactions on Power Delivery, vol. 12, no. 3, pp. 1128-1136, July 1997. [5] M. R. Zaman and M. A. Rahman, "Experimental testing of the artificial neural network based protection of power transformers," IEEE Transactions on Power Delivery, vol. 13, no. 2, pp. 510-517, Apr. 1998. [6] Chien-hsing lee, yaw-juen wang, wenliang huang, “A Literature Survey of Wavelets in Power Engineering Applications”, Proc. Natl. Sci. Counc. ROC(A), Vol. 24, No. 4, 2000. pp. 249-258. [7] M. G. Morante and D. W. Nocoletti, "A wavelet - based differential transformer protection," IEEE Transactions on Power Delivery, vol. 14, no. 4, pp. 1351-1358, October 1999. [8] O. A. S. Youssef, "A wavelet-base technique for discrimination between faults and magnetizing inrush currents in transformers," IEEE Transactions on Power Delivery, vol. 18, no. 1, pp. 170-176, January 2003. [9] A R Sedighi, M.R. Haghifam, " Detection of inrush current in distribution transformer using wavelet transform", Electrical Power and Energy Systems 27 (2005), pp. 361-370 Elsevier [10] Y. Zhang, X. Ding, Y. Liu, and P. J. Griffin, "An artificial neural network approach to transformer fault diagnosis," IEEE Transactions on Power Delivery, vol. 11, no. 4, pp. 1836-1841, October 1996. [11] Atthapol Ngaopitakkul and Anantawat Kunakorn, "Internal Fault Classification in Transformer Windings using Combination of Discrete Wavelet Transforms and Back-propagation Neural Networks", International Journal of Control, Automation and Systems, vol. 4, no. 3, pp. 365-371, June 2006