Fault Zone Detection on Advanced Series Compensated Transmission Line using Discrete Wavelet Transform and SVM
In this paper a novel method for finding the fault zone
on a Thyristor Controlled Series Capacitor (TCSC) incorporated
transmission line is presented. The method makes use of the Support
Vector Machine (SVM), used in the classification mode to
distinguish between the zones, before or after the TCSC. The use of
Discrete Wavelet Transform is made to prepare the features which
would be given as the input to the SVM. This method was tested on a
400 kV, 50 Hz, 300 Km transmission line and the results were highly
accurate.
[1] Y. H. Song, A. T. Johns, Q. Y. Xuan, "Artificial neural-network
based protection scheme for controllable series-compensated EHV
transmission line", Proc. Inst. Elect. Eng.-Gener. Transm. Distrib.,
vol. 143, no. 6, pp. 535-540, Nov. 1996.
[2] B. Kasztenny,"Distance protection of series compensated lines-
problems and solutions,"Proc. 28th Annu. Western Protective Relay
Conf., Spokane, WA, Oct. 22-25,2001,pp.1-36.
[3] D. Nvosel, A. Phadke, M. M. Saha, and S. Lindhal, "Problems and
solutions for microprocessor protection of series compensated
lines," Proc. 6th Inf. Conf. Developments in Power System
Protection, Mar. 25-27, 1997, pp. 18-23, Conf Pub No. 434.
[4] D. W. P. Thomas and C. Christopulos, "Ultra-high speed protection
of series compensated lines," IEEE Trans. Power Del., vol. 7, no. 1,
pp. 139-145, Jan. 1992.
[5] P.K. Dash, S.R. Samantray, and Ganapati Panda, "Fault
Classification and Section Identification of an Advanced Series-
Compensated Transmission Line Using Support Vector Machine",
IEEE Trans. on Power Del., vol. 22, no. 1, pp. 67-73, Jan 2007.
[6] Urmil B. Parikh, Biswarup Das, Rudra Pratap
Maheshwari,"Comnined Wavelet-SVM Technique for Fault Zone
Detection in a Series Compensated Transmission Line," IEEE
Trans. Power Del., vol. 23, no. 4, Oct 2008.
[7] S. R. Samantaray ," Decision tree-based fault zone identification and
fault classification in flexible AC transmissions-based transmission
line,"IET Gener. Transm. Distrib.,vol. 3, Iss.5, pp. 425-436, 2009.
[8] Vladimir N. Vapnik, "An Overview of Statistical Learning Theory",
IEEE Transactions on Neural Networks, vol. 10, no.5, pp 988-999 ,
Sept. 1999
[9] C. Cortes and V. Vapnik, "Support Vector Networks", Int.
Proceedings of Machine Learning, vol. 20, no. 3, pp 273-
297, 1995
[10] V. Vapnik, The Nature of Statistical Learning Theory,
Springer, 1995.
[11] C. C. Burges, "A tutorial on support vector machines for pattern
recognition", In Proceedings of Int. Conference on Data Mining and
Knowledge Discovery, vol. 2, no. 2, pp. 121-167, 1998.
[12] "PSCAD/EMTDC Power Systems Simulation Manual", 1997,
Winnipeg, MB, Canada.
[13] Chang C. C. and Chin J. L., LIBSVM:A library for Support Vector
Machines, 2001. Software available at
http://www.csie.ntu.edu.tw/~cjlin/libsvm.
[1] Y. H. Song, A. T. Johns, Q. Y. Xuan, "Artificial neural-network
based protection scheme for controllable series-compensated EHV
transmission line", Proc. Inst. Elect. Eng.-Gener. Transm. Distrib.,
vol. 143, no. 6, pp. 535-540, Nov. 1996.
[2] B. Kasztenny,"Distance protection of series compensated lines-
problems and solutions,"Proc. 28th Annu. Western Protective Relay
Conf., Spokane, WA, Oct. 22-25,2001,pp.1-36.
[3] D. Nvosel, A. Phadke, M. M. Saha, and S. Lindhal, "Problems and
solutions for microprocessor protection of series compensated
lines," Proc. 6th Inf. Conf. Developments in Power System
Protection, Mar. 25-27, 1997, pp. 18-23, Conf Pub No. 434.
[4] D. W. P. Thomas and C. Christopulos, "Ultra-high speed protection
of series compensated lines," IEEE Trans. Power Del., vol. 7, no. 1,
pp. 139-145, Jan. 1992.
[5] P.K. Dash, S.R. Samantray, and Ganapati Panda, "Fault
Classification and Section Identification of an Advanced Series-
Compensated Transmission Line Using Support Vector Machine",
IEEE Trans. on Power Del., vol. 22, no. 1, pp. 67-73, Jan 2007.
[6] Urmil B. Parikh, Biswarup Das, Rudra Pratap
Maheshwari,"Comnined Wavelet-SVM Technique for Fault Zone
Detection in a Series Compensated Transmission Line," IEEE
Trans. Power Del., vol. 23, no. 4, Oct 2008.
[7] S. R. Samantaray ," Decision tree-based fault zone identification and
fault classification in flexible AC transmissions-based transmission
line,"IET Gener. Transm. Distrib.,vol. 3, Iss.5, pp. 425-436, 2009.
[8] Vladimir N. Vapnik, "An Overview of Statistical Learning Theory",
IEEE Transactions on Neural Networks, vol. 10, no.5, pp 988-999 ,
Sept. 1999
[9] C. Cortes and V. Vapnik, "Support Vector Networks", Int.
Proceedings of Machine Learning, vol. 20, no. 3, pp 273-
297, 1995
[10] V. Vapnik, The Nature of Statistical Learning Theory,
Springer, 1995.
[11] C. C. Burges, "A tutorial on support vector machines for pattern
recognition", In Proceedings of Int. Conference on Data Mining and
Knowledge Discovery, vol. 2, no. 2, pp. 121-167, 1998.
[12] "PSCAD/EMTDC Power Systems Simulation Manual", 1997,
Winnipeg, MB, Canada.
[13] Chang C. C. and Chin J. L., LIBSVM:A library for Support Vector
Machines, 2001. Software available at
http://www.csie.ntu.edu.tw/~cjlin/libsvm.
@article{"International Journal of Electrical, Electronic and Communication Sciences:59647", author = "Renju Gangadharan and G. N. Pillai and Indra Gupta", title = "Fault Zone Detection on Advanced Series Compensated Transmission Line using Discrete Wavelet Transform and SVM", abstract = "In this paper a novel method for finding the fault zone
on a Thyristor Controlled Series Capacitor (TCSC) incorporated
transmission line is presented. The method makes use of the Support
Vector Machine (SVM), used in the classification mode to
distinguish between the zones, before or after the TCSC. The use of
Discrete Wavelet Transform is made to prepare the features which
would be given as the input to the SVM. This method was tested on a
400 kV, 50 Hz, 300 Km transmission line and the results were highly
accurate.", keywords = "Flexible ac transmission system (FACTS), thyristorcontrolled series-capacitor (TCSC), discrete wavelet transforms(DWT), support vector machine (SVM).", volume = "4", number = "9", pages = "1403-5", }