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Knowledge-Based Approach Using Support Vector Machine for Transmission Line Distance Relay Co-ordination
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 Title & Authors
Knowledge-Based Approach Using Support Vector Machine for Transmission Line Distance Relay Co-ordination
Ravikumar, B.; Thukaram, D.; Khincha, H.P.;
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In this paper, knowledge-based approach using Support Vector Machines (SVMs) are used for estimating the coordinated zonal settings of a distance relay. The approach depends on the detailed simulation studies of apparent impedance loci as seen by distance relay during disturbance, considering various operating conditions including fault resistance. In a distance relay, the impedance loci given at the relay location is obtained from extensive transient stability studies. SVMs are used as a pattern classifier for obtaining distance relay co-ordination. The scheme utilizes the apparent impedance values observed during a fault as inputs. An improved performance with the use of SVMs, keeping the reach when faced with different fault conditions as well as system power flow changes, are illustrated with an equivalent 265 bus system of a practical Indian Western Grid.
Apparent impedance loci;Distance relay;Fault resistance and knowledge-base;Support vector machines;Zonal co-ordination;
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Fault location and detection techniques in power distribution systems with distributed generation: A review, Renewable and Sustainable Energy Reviews, 2017, 74, 949  crossref(new windwow)
Comparison of Multiclass SVM Classification Methods to Use in a Supportive System for Distance Relay Coordination, IEEE Transactions on Power Delivery, 2010, 25, 3, 1296  crossref(new windwow)
A. G. Phadke and J. S. Thorp, "Expose hidden failures to prevent cascading outages," Computer Application in Power, IEEE, pp. 20-23, July 1996

T. S. Sidhu, D. S. Baltazar, R. M. Palomino, and M. S. Sachdev, "A new approach for calculating zone-2 setting of distance relays and its use in an adaptive protection system," Power Delivery, IEEE Trans,. vol. 19 (1), pp. 70-77, Jan. 2004 crossref(new window)

C.-H. Kim, J.-Y. Heo, and R. K. Aggarwal, "An enhanced zone 3 algorithm of a distance relay using transient components and state diagram," Power Delivery, IEEE Transactions on, vol. 20, no. 1, pp. 39-46, Jan. 2005 crossref(new window)

E.-A. Khalil, J. Geza, T. M. Donald, and R. Brearley, "Comprehensive transmission distance protection settings using an intelligent-based analysis of events and consequences," Power Delivery, IEEE Transactions on, vol. 20, no. 3, pp. 1817-1824, July 2005 crossref(new window)

S. H. Horowitz and A. G. Phadke, "Third zone revisited," Power Delivery, IEEE Transactions on, vol. 21, no. 1, pp. 23- 29, Jan. 2006 crossref(new window)

K.-H. Tseng, W.-S. Kao, and J.-R. Lin, "Load model effects on distance relay settings," Power Delivery, IEEE Trans., vol. 18 (4), pp. 1140-1146, Oct. 2003 crossref(new window)

S. A. Soman, T. B. Nguyen, M. A. Pai, R. Vaidyanathan, "Analysis of angle stability problems: a transmission protection systems perspective," Power Delivery, IEEE Trans., vol. 19 (3), pp. 1024-1033, July. 2004 crossref(new window)

Li, K.K., Lai, L.L., David, A.K., "Stand alone intelligent digital distance relay", Power Systems, IEEE Trans., Vol. 15 (1), pp. 137-142, Feb. 2000 crossref(new window)

B. R. Bhalja and R. P. Maheshwari, "High-resistance faults on two terminal parallel transmission lines: analysis, simulation studies, and an adaptive distance relaying scheme," Power Delivery, IEEE Transactions on, vol. 22 (2), pp. 801-812, Apr. 2007 crossref(new window)

E. Orduna, F. Garces, and E. Handschin, "Algorithmic-knowledge-based adaptive coordination in transmission protection," Power Delivery, IEEE Transactions on, vol. 18, no. 1, pp. 61-65, Jan. 2003 crossref(new window)

S. M. Brahma, "Distance relay with out-of-step blocking function using wavelet transform," Power Delivery, IEEE Trans., vol. 22 (3), pp. 1360-1366, July 2007 crossref(new window)

IEEE guide for protective relay applications to transmission lines, Feb. 2000

M. J. Damborg; R. Ramaswami; S. S. Venkata; and J. M. Postforoosh, "Computer aided transmission protection system design part 1: algorithms," IEEE Transactions on Power Apparatus and Systems, vol. PAS-103, no. 1, pp. 51-59, Jan. 1984 crossref(new window)

R. Ramaswami; S. S. Venkata; M. J. Damborg; J. M. Postforoosh, "Computer aided transmission protection system design part ii: implementation and results," IEEE Trans. Power Apparatus and Systems, vol. PAS-103, no. 1, pp. 60-65, Jan. 1984 crossref(new window)

R. Vaidyanathan and S. A. Soman, "Distance relay coordination considering power swings," in Proc. Int. Conf. Power Syst. Commun. Syst. Infrastructures for Future, 2002

Transient Stability software Program, developed by Prof. D. Thukaram at the Department of Electrical Engineering, Indian Institute of Science, Bangalore, India

V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995

V. Vapnik, Statistical Learning Theory. Wiley, New York, NY, 1998

Smola and B. Scholkopf. (1998) A Tutorial on Support Vector Regression, Neurocolt Tech. Rep. NV2-TR- 1998-030. Available:

C. B. Schölkopf and A. Smola, "Advances in Kernel Methods-Support Vector Learning," Cambridge, MA: MIT Press, 1999

P. S. Sastry, "An Introduction to Support Vector Machine, Chapter from Computing and Information Sciences: Recent Trends," Narosa Publishing House, New Delhi, 2003

C. J. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 955-974, 1998

T. Joachims, "Making Large-scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning," Cambridge, MA, 1998. MIT Press

J. Platt, "Fast Training of Support Vector Machines using Sequential Minimal Optimization, Advance in Kernel Methods: Support Vector Learning," pp. 185-208, MIT Press, Cambridge, MA, 1999. Available at:

J. Weston and C. Watkins, "Multi-class support vector machines," Technical Report CSD-TR-98-04, Department of Computer Science, Royal Holloway, University of London, Egham, TW20 0EX, UK, 1998.

J. Platt, N. Cristianini, and J. Shawe-Taylor, "Large margin dags for multiclass classification," in Advances in Neural Information Processing Systems, 12th ed. Cambridge, MA: MIT Press, 2000

C. Campbell, "Kernel methods: A survey of current techniques," Neurocomput., vol. 48, pp. 63-84, 2002 crossref(new window)

C.-C. Chang and C-J. Lin, LIBSVM: a library for support vector machines, 2001.


S. S. Keerthi and C.-J. Lin, "Asymptotic behaviors of support vector machines with Gaussian kernel," Neural Computation 15 (7), pp. 1667-1689, 2003 crossref(new window)