Abstract
This chapter presents a new approach for distance relaying of transmission line using machine intelligence technique such as Support Vector Machine (SVM). SVM is a relatively new computational learning method based on the statistical learning theory. The proposed technique is used for developing protection schemes for Thyristor Controlled Series Compensated (TCSC) Line using post fault current samples for half cycle (10 samples) from the inception of the fault and firing angle as inputs to the SVM. Three SVMs are trained to provide fault classification, ground detection and section identification respectively for the TCSC line. Also SVM is used for faulty phase selection and ground detection in large power transmission system without TCSC. The method uses post fault current and voltage samples for 1/4th cycle (5 samples) as inputs to SVM-1 to result faulty phase selection. SVM-2 is trained and tested with zero sequence components of fundamental, 3rd and 5th harmonic components of the post fault current signal to result the involvement of ground in the fault process. The polynomial and Gaussian kernel based SVMs are designed to provide most optimized boundary for classification. The classification test results from SVMs are accurate for simulation model as well as experimental set-up, and thus provides fast and robust protection scheme for distance relaying in transmission line.
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References
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining Knowledge Discovery 2(2), 121–167 (1998)
Burges, C.J.C.: Geometry and invariance in kernel based methods. In: Schoelkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods—Support Vector Learning, pp. 89–116. The MIT Press, Cambridge (1999)
Chapelle, O., Vapnik, V.N.: Model selection for support vector machines. In: Solla, S., Leen, T.K., Muller, K.-R. (eds.) Advances in Neural Information Processing Systems, vol. 12, pp. 230–236. The MIT Press, Cambridge (2000)
Bregler, C.: Learning and recognizing human dynamics in video sequences. In: Nevatia, R., Medioni, G. (eds.), Silver Spring, MD, pp. 568–574. IEEE Computer Society Press, Los Alamitos (1997)
Chapelle, O., Vapnik, V.N., Bousquet, O., Mukherjee, S.: Choosing multiple parameters for support vector machines. Mach. Learn. 46(1-3), 131–159 (2002)
Collobert, R., Bangio, S.: SVMTorch: a support vector machine for large-scale regression and classifcation problems. J. Mach. Learn. Res. 1, 143–160 (2001)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge (2000)
Yajima, Y., Ohi, H., Mori, M.: Extracting feature subspace for kernel based support vector machines. Tech. rep., Technical Report 2001–5, Department of Industrial Engineering and Management, Tokyo Institute of Technology (2001)
Mangasarian, O.L., Musicant, D.R.: Successive over relaxation for support vector machines. IEEE Trans. Neural Networks 10m, 1032–1037 (1999)
Vapnik, V.N.: The nature of statistical learning theory. Springer, New York (1999)
Ribeiro, B.: Support vector machines and RBF neural networks for fault detection and diagnosis. In: Proceedings of the 8th international conference on neural information processing, paper 191 (2001)
Cawley, G.C., Talbot, N.L.C.: Fast exact leave-one-out cross-validation of sparse least-squares support vector machines. Neural Networks 17, 1467–1475 (2004)
Joachims, T.: Estimating the generalization performance of a SVM efficiently. In: Proc. 17th international conf. Machine learning. Morgan Kaufman, San Francisco (2000)
Kearns, M., Ron, D.: Algorithmic stability and sanity-check bounds for leave-one-out cross validation. In: Proc. 10th conf. comput. Learning theory, pp. 152–162. ACM, New York (1997)
Helbing, S.G., Karady, G.G.: Investigations of an advanced Form of Series Compensation. IEEE Trans. on Power Delivery 9(2), 939–946 (1994)
Larsen, E.V., Clark, K., Miske, S.A., Urbanek, J.: Characteristics and Rating Considerations of Thyristor Controlled Series Compensation. IEEE Transactions on Power Delivery 9(2), 992–1000 (1994)
Noroozian, M., Angquist, L., Ghandhari, M., Anderson, G.: Improving Power System Dynamics by series connected FACTS devices. IEEE Trans. on Power Delivery 12(4), 1635–1641 (1997)
Dash, P.K., Pradhan, A.K., Panda, G., Liew, A.C.: Adaptive relay setting for flexible AC Transmission Systems (FACTS). IEEE Trans. on Power delivery 15(1), 38–43 (2000)
Song, Y.H., Johns, A.T., Xuan, Q.Y.: Artificial Neural Network Based Protection Scheme for Controllable series-compensated EHV transmission lines. IEE Proc. Gen. Trans. Dist. 143(6), 535–540 (1996)
Song, Y.H., Xuan, Q.Y., Johns, A.T.: Protection Scheme for EHV Transmission Systems with Thyristor Controlled Series Compensation using Radial Basis Function Neural Networks. Electric Machines and Power Systems, 553–565 (1997)
Song, Y.H., Xuan, Q.Y., Johns, A.T.: Protection of scheme for EHV transmission systems with thyristor controlled series compensation using radial basis function neural networks. Electric machines and power systems 25, 553–565 (1997)
Song, Y.H., Johns, A.T., Xuan, Q.Y.: Artificial Neural Network Based Protection Scheme for Controllable series-compensated EHV transmission lines. IEE Proc. Gen. Trans. Disbn. 143(6), 535–540 (1996)
Yaussef, O.A.S.: New Algorithms for phase selection based on wavelet transforms. IEEE transactions on Power Delivery 17, 908–914 (2002)
Yaussef, O.A.S.: Online applications of wavelet transform to power system relaying. IEEE transactions on Power Delivery 18(4), 1158–1165 (2003)
Yaussef, O.A.S.: Combined Fuzzy-Logic wavelet based fault classification for power system relaying. IEEE transaction on Power Delivery 19(2), 582–589 (2004)
Osman, A.H., Malik, O.P.: Transmission line protection based on wavelet transform. IEEE transaction on Power Delivery 19(2), 515–523 (2004)
Martin, F., Aguado, J.A.: Wavelet based ANN approach for Transmission line protection. IEEE transaction on Power Delivery 18(4), 1572–1574 (2003)
Chanda, D., Kishore, N.K., Sinha, A.K.: A wavelet multi-resolution analysis for location of faults on transmission lines. Electric Power and Energy systems 25, 59–69 (2003)
Boolen, M.H.J.: Traveling wave based protection of double circuit lines. Proceeding Inst. Elect. Engg. C 140(1), 37–47 (1993)
Dalstein, T., Kluicke, B.: Neural Network approach to fault classification for high speed protection relaying. IEEE transaction, P&D 10(2), 1002–1011 (1995)
Dash, P.K., Pradhan, A.K., Panda, G.: A novel Fuzzy Neural Network based distance relaying scheme. IEEE Transaction on Power Delivery 15(3), 902–907 (2000)
Martin, F., Aguado, J.A.: Wavelet based ANN approach for Transmission line protection. IEEE transaction on Power Delivery 18(4), 1572–1574 (2003)
Joachims, T.: Text categorization with support vector machines: Learning with many relevant features. Technical report, University of Dortmund (1997)
Salomon, J.: Support vector machines for phoneme classification. M.Sc Thesis, University of Edinburgh (2001)
Dash, P.K., Samantaray, S.R.: An accurate fault classification algorithm using a minimal radial basis function neural network. International journal of engineering intelligent systems 12(4), 205–210 (2004)
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Samantaray, S.R., Dash, P.K., Panda, G. (2010). Power System Protection Using Machine Learning Technique. In: Panigrahi, B.K., Abraham, A., Das, S. (eds) Computational Intelligence in Power Engineering. Studies in Computational Intelligence, vol 302. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14013-6_7
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DOI: https://doi.org/10.1007/978-3-642-14013-6_7
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