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A Novel Approach to Fault Classification and Fault Location for Medium Voltage Cables Based on Artificial Neural Network

Authors: H. Khorashadi-Zadeh, M. R. Aghaebrahimi

Abstract:

A novel application of neural network approach to fault classification and fault location of Medium voltage cables is demonstrated in this paper. Different faults on a protected cable should be classified and located correctly. This paper presents the use of neural networks as a pattern classifier algorithm to perform these tasks. The proposed scheme is insensitive to variation of different parameters such as fault type, fault resistance, and fault inception angle. Studies show that the proposed technique is able to offer high accuracy in both of the fault classification and fault location tasks.

Keywords: Artificial neural networks, cable, fault location andfault classification.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1085523

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References:


[1] Z. Q. Bo, A. T. Johns, "A new non-unit protection scheme based on fault generated high frequency current signals," APSCOM-95, International Conference on Advances in Power System Control, Operation & Management, 9-11 November, 1995, Hong Kong.
[2] R.N. Mahanty, P.B.D Gupta, "Application of RBF neural network to fault classification and location in transmission lines," IEE Proceedings Generation, Transmission and Distribution, Vol. 151, 2 March 2004, pp. 201- 212.
[3] M. Oleskovicz, D.V. Coury, R.K. Aggarwal, "A complete scheme for fault detection, classification and location in transmission lines using neural networks," Developments in Power System Protection, 2001, Seventh International Conference on (IEE), 9-12 April 2001, pp. 335- 338.
[4] H. Khorashadi-Zadeh, S. HOSSEINI "An accurate fault locator for cable transmission using ANN," 12th IEEE Mediterranean IEEE Electrotechnical Conference, Melcon2004, Dubrovink, Croatia, pp. 901- 904.
[5] H. Khorashadi-Zadeh, "Correction of capacitive voltage transformer distorted secondary voltages using artificial neural networks," In Proceedings Seventh Seminar on Neural Network Applications in Electrical Engineering, Sep. 2004, Belgrad-serbia and Montenegro (Neural 2004).
[6] M. Kezonuic, "A Survey of neural net application to protective relaying and fault analysis," Eng. Int. Sys. vol. 5, no. 4, Dec. 1997, pp. 185-192.
[7] H. Khorashadi Zadeh, M. Sanaye-Pasand "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, pp. 747-753.
[8] 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, pp. 373-377.
[9] H. Khorashadi-Zadeh, et. al. "AN ANN Based Approach to Improve the Distance Relaying Algorithm," Proc. of 2004 IEEE Cybernetics and Intelligent Systems Conference, Dec. 2004, Singapoure, (CIS2004).
[10] V. H. Ortiz, et. al. "Arcing faults patterns for based ANN relays for transmission lines," Proc. 2003 IEEE PowerTech Conference, June 23- 26, Bologna, Italy.
[11] D.V. Coury, and D.C. Jorge, "Artificial neural network approach to distance protection," IEEE Trans. on Power Delivery, vol. 13, no. 1, 1998, pp. 102-108.
[12] K. R. Cho, et. al "An ANN based approach to improve the speed of a differential equation based distance relaying algorithm," IEEE Trans. on Power Delivery, vol. 14, Apr. 1999, pp. 349-357.
[13] PSCAD/EMTDC User-s Manual, Manitoba HVDC Research Center, Winnipeg, Manitoba, Canada.
[14] S. Haykin, Neural Networks, IEEE Press, New York, 1994.
[15] M. T. Hagan and M. B. Menhaj, "Training feedforward networks with the Marquardt algorithm," IEEE Trans. on Neural Networks, vol. 5, no. 6, 1994, pp. 989-993.