A differential relay for power transformers using intelligent tools

This paper presents alternative approaches using artificial neural networks (ANNs) for the protection of power transformers. A complete protection scheme was implemented. An ANN subroutine was used to discriminate internal faults from other situations, replacing the traditional Fourier method for harmonic restraint. In addition, a routine for reconstruction of saturated current signals based on recurrent ANNs is also proposed. The proposed methods were extensively tested and then compared to the traditional differential protection algorithm, showing promising results. The application of the ANN tools is a new and important stage in the differential relay analysis methodology for power transformer protection

[1]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[2]  Denis V. Coury,et al.  Modeling a power transformer for investigation of digital protection schemes , 1998, 8th International Conference on Harmonics and Quality of Power. Proceedings (Cat. No.98EX227).

[3]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[4]  Hong-Jun Yoon,et al.  Correction of current transformer distorted secondary currents due to saturation using artificial neural networks , 2001 .

[5]  R. Yacamini,et al.  The calculation of inrush current in three-phase transformers , 1986 .

[6]  Drago Dolinar,et al.  Improved operation of power transformer protection using artificial neural network , 1997 .

[7]  M. S. Sachdev,et al.  Using a neural network for transformer protection , 1995, Proceedings 1995 International Conference on Energy Management and Power Delivery EMPD '95.

[8]  Z. Q. Bo,et al.  Identification of electromagnetic transients in power transformer system using artificial neural network , 1998, POWERCON '98. 1998 International Conference on Power System Technology. Proceedings (Cat. No.98EX151).

[9]  P. Bertrand,et al.  Neural networks: a mature technique for protection relays , 1997 .

[10]  Geoffrey E. Hinton,et al.  Learning representations by back-propagation errors, nature , 1986 .

[11]  Zoran Obradovic,et al.  Training an artificial neural network to discriminate between magnetizing inrush and internal faults , 1994 .

[12]  P. Bastard,et al.  Neural network-based algorithm for power transformer differential relays , 1995 .

[13]  T. H. Barton,et al.  Three Phase Transformer Transients , 1974 .

[14]  M. S. Sachdev,et al.  A power transformer protection technique with stability during current transformer saturation and ratio-mismatch conditions , 1999 .

[15]  R.K. Aggarwal,et al.  An Algorithm for Compensating Secondary Currents of Current Transformers , 1997, IEEE Power Engineering Review.

[16]  A. T. Johns,et al.  Development and hardware implementation of a compensating algorithm for the secondary current of current transformers , 1996 .

[17]  D. C. Yu,et al.  Simplified artificial neural network structure with the current transformer saturation detector provides a good estimate of primary currents , 2000, 2000 Power Engineering Society Summer Meeting (Cat. No.00CH37134).

[18]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[19]  Hong-Jun Yoon,et al.  Neural network for current transformer saturation correction , 1999, 1999 IEEE Transmission and Distribution Conference (Cat. No. 99CH36333).

[20]  M. A. Marin,et al.  A comparative analysis of digital relaying algorithms for the differential protection of three phase transformers , 1988 .