Automatic Identification of Faults in Power Systems Using Neural Network Technique

The main objective involved with this paper consists of presenting the results obtained from the application of artificial neural networks and statistical tools in the automatic identification and classification process of faults in electric power distribution systems. The developed techniques to treat the proposed problem have used, in an integrated way, several approaches that can contribute to the successful detection process of faults, aiming that it is carried out in a reliable and safe way. The compilations of the results obtained from practical experiments accomplished in a pilot radial distribution feeder have demonstrated that the developed techniques provide accurate results, identifying and classifying efficiently the several occurrences of faults observed in the feeder.

[1]  J.A. Momoh,et al.  A neural net based approach for fault diagnosis in distribution networks , 1999, IEEE Power Engineering Society. 1999 Winter Meeting (Cat. No.99CH36233).

[2]  Ali Abur,et al.  A new fault location technique for radial distribution systems based on high frequency signals , 1999, 1999 IEEE Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.99CH36364).

[3]  Chan-Gook Park,et al.  Detection of high impedance faults using neural nets and chaotic degree , 1998, Proceedings of EMPD '98. 1998 International Conference on Energy Management and Power Delivery (Cat. No.98EX137).

[4]  Chao-Shun Chen,et al.  Coloured Petri nets approach for solving distribution system contingency by considering customer load patterns , 2001 .

[5]  M. K. Celik Integration of advanced applications for distribution automation , 1999, IEEE Power Engineering Society. 1999 Winter Meeting (Cat. No.99CH36233).

[6]  T. S. Davies,et al.  A mathematical approach for identification of fault sections on the radial distribution systems: voltage sensor , 2000, 2000 10th Mediterranean Electrotechnical Conference. Information Technology and Electrotechnology for the Mediterranean Countries. Proceedings. MeleCon 2000 (Cat. No.00CH37099).

[7]  Jarmo Partanen,et al.  Using fuzzy sets to model the uncertainty in the fault location process of distribution networks , 1994 .

[8]  Pertti Järventausta,et al.  Novel algorithms for earth fault indication based on monitoring of shunt resistance of MV feeder as a part of relay protection , 2001 .

[9]  P. Jarventausta,et al.  Methods for earth fault identification and distance estimation in a compensated medium voltage distribution network , 1998, Proceedings of EMPD '98. 1998 International Conference on Energy Management and Power Delivery (Cat. No.98EX137).

[10]  W.-H.E. Liu,et al.  A fuzzy set method for fault location identification in power distribution systems , 1996, Proceedings of 35th IEEE Conference on Decision and Control.

[11]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[12]  E. Handschin,et al.  Knowledge based alarm handling and fault location in distribution networks , 1991, [Proceedings] Conference Papers 1991 Power Industry Computer Application Conference.