This paper describes a new approach for power transformer differential protection which is based on the wave-shape recognition technique. An algorithm based on neural network principal component analysis (NNPCA) with back-propagation learning is proposed for digital differential protection of power transformer. The principal component analysis is used to preprocess the data from power system in order to eliminate redundant information and enhance hidden pattern of differential current to discriminate between internal faults from inrush and overexcitation conditions. This algorithm has been developed by considering optimal number of neurons in hidden layer and optimal number of neurons at output layer. The proposed algorithm makes use of ratio of voltage to frequency and amplitude of differential current for transformer operating condition detection. This paper presents a comparative study of power transformer differential protection algorithms based on harmonic restraint method, NNPCA, feed forward back propagation neural network (FFBPNN), space vector analysis of the differential signal, and their time characteristic shapes in Park’s plane. The algorithms are compared as to their speed of response, computational burden, and the capability to distinguish between a magnetizing inrush and power transformer internal fault. The mathematical basis for each algorithm is briefly described. All the algorithms are evaluated using simulation performed with PSCAD/EMTDC and MATLAB. 1. Introduction Power transformer is one of the most important components in power system, for which various types of protective and monitoring schemes have been developed for many years. Differential protection is one of the most widely used methods for protecting power transformer against internal faults. The technique is based on the measurement and comparison of currents at both side of transformer: primary and secondary lines. The differential relay trips whenever the difference of the currents in both sides exceeds a predetermined threshold. This technique is accurate in most of the cases of transformer internal faults however mal-operation of differential relay is possible due to inrush currents, which result from transients in transformer magnetic flux. The transients in transformer magnetic flux may occur due to energization of transformer, voltage recovery after fault clearance or connection of parallel transformers. The existence of such current disturbances has made the protection of power transformers a challenging problem for protection engineers. Therefore, accurate
References
[1]
M. Tripathy, R. P. Maheshwari, and H. K. Verma, “Advances in transformer protection: a review,” Electric Power Components and Systems, vol. 33, no. 11, pp. 1203–1209, 2005.
[2]
P. Arboleya, G. Díaz, J. Gómez-Aleixandre, and C. González-Morán, “A solution to the dilemma inrush/fault in transformer differential relaying using MRA and wavelets,” Electric Power Components and Systems, vol. 34, no. 3, pp. 285–301, 2006.
[3]
H. K. Verma and G. C. Kakoti, “Algorithm for harmonic restraint differential relaying based on the discrete Hartley transform,” Electric Power Systems Research, vol. 18, no. 2, pp. 125–129, 1990.
[4]
M. C. Shin, C. W. Park, and J. H. Kim, “Fuzzy logic-based relaying for large power transformer protection,” IEEE Transactions on Power Delivery, vol. 18, no. 3, pp. 718–724, 2003.
[5]
S. A. Saleh and M. A. Rahman, “Modeling and protection of a three-phase power transformer using wavelet packet transform,” IEEE Transactions on Power Delivery, vol. 20, no. 2, pp. 1273–1282, 2005.
[6]
S. Ala, M. Tripathy, and A. K. Singh, “Identification of internal faults in power transformer using symmetrical components and Park's plots,” in Proceedings of IEEE International Conference on Power Systems, IIT, Kharagpur, India, December 2009.
[7]
X. Ma and J. Shi, “New method for discrimination between fault and magnetizing inrush current using HMM,” Electric Power Systems Research, vol. 56, no. 1, pp. 43–49, 2000.
[8]
D. Barbosa, U. C. Netto, D. V. Coury, and M. Oleskovicz, “Power transformer differential protection based on Clarke's transform and fuzzy systems,” IEEE Transactions on Power Delivery, vol. 26, no. 2, pp. 1212–1220, 2011.
[9]
J. Ma, Z. Wang, Q. Yang, and Y. Liu, “Identifying transformer inrush current based on normalized grille curve,” IEEE Transactions on Power Delivery, vol. 26, no. 2, pp. 588–595, 2011.
[10]
L. G. Perez, A. J. Flechsig, J. L. Meador, and Z. Obradovic, “Training an artificial neural network to discriminate between magnetizing inrush and internal faults,” IEEE Transactions on Power Delivery, vol. 9, no. 1, pp. 434–441, 1994.
[11]
P. Bastard, M. Meunier, and H. Regal, “Neural network-based algorithm for power transformer differential relays,” IEE Proceedings, vol. 142, no. 4, pp. 386–392, 1995.
[12]
J. E. Jackson, A User's Guide to Principal components, Wiley, Hoboken, NJ, USA, 2003.
[13]
S. Haykin, Neural Network: A Comprehensive Foundation, Pearson Education, New Delhi, India, 2008.
[14]
C. M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, New York, NY, USA, 1995.
[15]
D. Woodford, Introduction To PSCAD V3, Manitoba HVDC Research Centre, Manitoba, Canada, 2001.
[16]
S. E. Zocholl, Analyzing and Applying Current Transformers, Schweitzer Engineering Laboratories, Pullman, Wash, USA, 2004.
[17]
M. S. Sachdev, “Microprocessor relays and protection systems,” IEEE Tutorial Course Text 88EH0269-1-PWR, 1988.
[18]
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. 510–515, 1998.
