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Accurate Fault Classification And Section Identification Scheme In Tcsc Compensated Transmission Line Using Svm

Authors: Pushkar Tripathi; Abhishek Sharma; G. N. Pillai; Indira Gupta;

Accurate Fault Classification And Section Identification Scheme In Tcsc Compensated Transmission Line Using Svm

Abstract

{"references": ["R. M. Mathur, and R. K. Varma, Thyristor-Based Facts Controllers for\nElectrical Transmission Systems, John Wiley & Sons, inc. Publication,\n2002.", "Q Y Xuan and A T Johns. \"Digital simulation of series-compensated\nEHV (extra high voltage) Transmission systems\" in Simulation of Power\nSystems, IEE Colloquium on, dec 1992, pp. 1-3.", "\"Single phase tripping and auto reclosing of transmission lines-ieee\ncommittee report,\" Power Delivery, IEEE Transactions on,vol. 7, no 1,\npp. 182-192, jan 1992.", "D. Thomas and C. Christopoulos, \"Ultra-High Speed Protection of\nSeries Compensated Lines\", Power Delivery, IEEE Transactions on,vol.\n7, no 1, pp. 139-145, jan 1992.", "A. Girgis, A. Sallam, A. El-Din, \"An Adaptive Protection Scheme for\nAdvanced Series Compensated (ASC) Transmission Lines\", Power\nDelivery, IEEE Transactions on,vol. 13, no. 2, pp. 414-420, April 1998.", "W. Cheong and R. Agrawal, \"A Novel Feature Extraction and\nOptimization Method for Neural Network-based Fault Classification in\nTCSC-Compensated Lines\", Power Engineering Society Summer\nMeeting, 2002 IEEE, vol. 2, july 2002 2002, pp. 795-800.", "Q. Y. Xuan, Y. H. Song, A. T. Johns, R. Morgan and D. Williams,\n\"Performance of an adaptive protection scheme for series compensated\nEHV transmission systems using neural networks\", Electric Power\nSystem Research., Vol. 36, no. 1, pp. 57-66, January 1996.", "Y. H. Song, A. T. Johns, Q. Y. Xuan, \"Radial Basis Function Neural\nNetworks for fault Diagnosis in Controllable Series Compensated\nTransmission Lines\", Electro-technical conference- MELECON '96, 8th\nMediterranean, Vol. 3, May 1996, pp. 13-16.", "A. K. Pradhan, A. Routray, S. Pati, and D. K. Pradhan, \" Wavelet Fuzzy\nCombined Approach for Fault Classification of a Series-Compensated\nTransmission Line\", Power Delivery, IEEE Transactions on, Vol. 19,\nno. 4, October 2004, pp. 1612-1618.\n[10] A. K. Pradhan, A. Routrsy, and B. Biswal, \"Higher Order Statistics-\nFuzzy Integrated Scheme for Fault Classification of a Series\nCompensated Transmission Line\", Power Delivery, IEEE Transactions\non, Vol. 19, April 2004, pp. 891-893.\n[11] B. Das, J.V.Reddy, \"Fuzzy-logic-based fault classification scheme for\ndigital distance protection\", Power Delivery, IEEE Transactions on,\nVol. 20, April 2005, pp.609-616.\n[12] Bhalja, Bhavesh and Maheshwari, R. P., \"Wavelet-based fault\nclassification scheme for a transmission line using a support vector\nmachine\", Electric Power Component and System, Vol. 36, no. 10, 2008,\npp.1017-1030.\n[13] Dash P. K., Samantaray, S. R.; Panda, G., \"Fault Classification and\nSection Identification of an Advanced Series-Compensated\nTransmission Line Using Support Vector Machine\", Power Delivery,\nIEEE Transactions on, Vol. 22, no. 1, January 2007, pp. 67-73.\n[14] U. B. Parikh, B. Das, R. Maheshwari, \"Fault classification technique for\nseries compensated transmission line using support vector machine\",\nInternational Journal of Electrical Power & Energy Systems, Vol. 32,\nno. 6, July 201, pp. 629-636.\n[15] Gangadharan R., Pillai G.N., Gupta I., \"Fault zone detection on\nadvanced series compensated transmission line using discrete wavelet\ntransform and SVM\", Proceedings of World Academy of Science,\nEngineering andTechnology, vol. 70, 2010, pp. 176-180.\n[16] H. Simon, NEURAL NETWORKS: A Comprehensive Foundatio, 2nd\nEd., Printice-Hall, Inc., 1999.\n[17] C. M. Bishop, Pattern Recognition and Machine Learning, Springer,\n2006.\n[18] PSCAD/EMTDC v 4.2.0, Manitoba HVDC Research center.\n[19] V. Vapnik, \"An Overview of Statistical Learning Theory,\" Neural\nNetworks, IEEE Transaction on, vol. 10, no. 5, September 1999,\npp. 988-999.\n[20] C. Cortes, and V. Vapnik, \"Support Vector Networks\", Machine\nLearning, Vol. 20, 1995, pp. 273-297.\n[21] C. W. Hsu and C. J. Lin, \"A Comparison of Methods for Multiclass\nSupport Vector Machines\", Neural Networks, IEEE Transaction on, vol.\n13, no. 2, March 2002, pp. 415- 425.\n[22] C. C. Chang and C. J. Lin, \"LIBSVM: A library for support vector\nmachines\", ACM Transactions on Intelligent Systems and Technology,\nvol. 2, 2011, pp. 1-27, Software available at http://www.csie.ntu.edu.tw/\n~cjlin/libsvm.\n[23] Y.H. Song, A.T. Johns, Q.Y. Xuan, \"Artificial neural-network-based\nprotection scheme for controllable series-compensated EHV\ntransmission lines\", Generation, Transmission and Distribution, IEE\nProceedings, Vol. 143, no. 6, November 1996, pp. 535- 540."]}

This paper presents a new approach for the protection of Thyristor-Controlled Series Compensator (TCSC) line using Support Vector Machine (SVM). One SVM is trained for fault classification and another for section identification. This method use three phase current measurement that results in better speed and accuracy than other SVM based methods which used single phase current measurement. This makes it suitable for real-time protection. The method was tested on 10,000 data instances with a very wide variation in system conditions such as compensation level, source impedance, location of fault, fault inception angle, load angle at source bus and fault resistance. The proposed method requires only local current measurement.

Keywords

Section Identification, Fault Classification, Feature Selection, Support Vector Machine (SVM), Thyristor-Controlled Series Compensator (TCSC)

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