Document Type: Research Paper




This paper presents the application of the fuzzy-neuro method to
investigate transformer inrush current. Recently, the frequency environment of
power systems has been made more complicated and the magnitude of the second
harmonic in inrush current has been decreased because of the improvement of cast
steel. Therefore, traditional approaches will likely mal-operate in the case of
magnetizing inrush with low second component and internal faults with high
second harmonic. The proposed scheme enhances the inrush detection sensitivity of
conventional techniques by using a fuzzy-neuro approach. Details of the design
procedure and the results of performance studies with the proposed detector are
given in the paper. The results of performance studies show that the proposed
algorithm is fast and accurate.


[1] U. D. Annakkage and P. G. McLaren et al, A current transformer model based on the Jiles-Atherton
theory of ferromagnetic hysteresis, IEEE Trans. Power Delivery, Jan. 2000.

[2] D. Chen, W. Chen, X. Yin, Z. Zhang and Y. Hu, The analysis of operation characteristic of
transformer differential protection based on virtual third harmonic theory, Proceedings of
International Conference on Power System Technology, PowerCon 2002, Vol. 2 , 13-17 Oct. (2002)
720 – 722.
[3] Electricity Training Association, Power System Protection, Vol. 2, Application, IEE, London, 1995.
[4] M. Gomez-Morante and D. W. Nicoletti, A wavelet-based differential transformer protection, IEEE
Trans. Power Delivery, Vol. 14, Oct. (1999) 1351–1358.
[5] H. Ichihashi, Learning in Hierarchical Fuzzy models by conjugate gradient Methode using
Bakpropagation Errors, Proc. of Intelligent System Symp., (1991) 235-240.
[6] B. Kasztenny and E. Rosolowski, A self-organizing fuzzy logic based protective relay an application to
power transformer protection, IEEE Trans. Power Delivery, Vol. 12, July (1997) 1119–1127.
[7] B. Kasztenny, E. Rosolowski and M. Lukowicz, Multi – objective optimization of a neural network
based differential relay for power transformers, IEEE transmission and distribution conference, Vo.l2,
Apr. (1999) 476-481.
[8] M. Kezonuic, A Survey of Neural Net Application to Protective Relaying and Fault Analysis, Eng. Int.
Sys. Vol. 5, No. 4, Dec. (1997) 185-192.
[9] M. Kezonovic and Y. Guo, Modeling and Simulation of the Power Transformer Faults and Related
Protective Relay Behavior, IEEE Trans. Power Delivery, Vol. 15, Jan. (2000) 44–50.
[10] H. Khorashadi Zadeh, A Novel Approach to Detection High Impedance Faults Using Artificial Neural
Network, Proc. of the 39nd International Universities Power Engineering Conference, UPEC2004, Sep.
(2004) 373-377.
[11] H. Khorashadi-Zadeh, Correction of Capacitive Voltage Transformer Distorted Secondary Voltages
Using Artificial Neural Networks, In Proceedings of Seventh Seminar on Neural Network Applications
in Electrical Engineering, Sep. 2004, Belgrad-serbia and Montenegro (Neural 2004).
[12] H. Khorashadi-Zadeh and M. R. Aghaebrahimi, AN ANN Based Approach to Improve the Distance
Relaying Algorithm, in Proceedings of Cybernetics and Iintelligent Systems Conference, Singapoure,
Dec. 2004, (CIS2004).
[13] H. Khorashadi Zadeh, Power Transformer Differential Protection Scheme Based on Wavelet
Transform and Artificial Neural Network Algorithms, Proc. of the 39nd International Universities
Power Engineering Conference, UPEC2004, (2004) 747-753.
[14] C. C. Lee, Fuzzy Logic in Control System: Fuzzy Logic controller-part I, IEEE Transmission on
System, Man. and Cybernetics, Vol. 20, No.2, 1 April (1990) 404-418.
[15] P. Liu, et. al., Study of Non Operation for Internal Faults Of Second-Harmonic Restraint Differential
Protection of Power Transformers, Transactions of the Engineering and Operation Division of the
Canadian Electrical Association, Vol. 28, Part 4, March (1998) 1-11.
[16] P. L. Mao, et al., A novel approach to the classification of the transient phenomena in power
transformers using combined wavelet transform and neural network, IEEE Transactions on Power
Delivery, Vol. 16, Issue: 2, April (2001) 654 – 659.
[17] M. Nagpal, M. S. Sachdev, K. Ning and L.M. Wedephol, Using a neural network for transformer
protection, IEEE Proc. of EMPD International Conference, Vol. 2, Nov. (1995) 674-679.
[18] L. D. Periz, A. J. Flechsig, J. L. Meador and Z. Obradovic, Training an artificial neural network to
discriminate between magnetizing inrush and internal faults, IEEE Trans. Power Delivery, Vol. 9, Jan.
(1994) 434–441.
[19] PSCAD/EMTDC User’s Manual, Manitoba HVDC Research Center, Winnipeg, Manitoba, Canada.
[20] M. A. Rahman and B. Jeyasurya, A state-of-art review of transformer protection algorithm, IEEE
Trans. Power Delivery, Vol. 3, Apr. (1988) 534–544.
[21] Myong-Chul Shin, Chul-Won Park and Jong-Hyung Kim, Fuzzy logic-based relaying for large power
transformer protection, IEEE Transactions on Power Delivery, Vol. 18, Issue: 3, July (2003)
718 – 724.
[22] Hu Yufeng, Chen Deshu, Yin Xianggen and Zhang Zhe, A novel theory for identifying transformer
magnetizing inrush current, Proceedings of International Conference on Power System Technology,
PowerCon 2002, Vol. 3 , 13-17 Oct. (2002) 1411-1415.