A combined protective scheme for fault classification and identification of faulty section in series compensated transmission lines
The fault detection process is very difficult in transmission lines with a fixed series capacitor because of the nonlinear behavior of protection device and series-parallel resonance. This paper proposes a new method based on S-transform (ST) and support vector machines (SVMs) for fault classification and identification of a faulty section in a transmission line with a fixed series capacitor placed at the middle of the line. In the proposed method, the fault detection process is carried out by using distinctive features of 3-line signals (line voltages and currents) and zero sequence current. The relevant features of these signals are obtained by using the ST. The obtained features are then used as input to multiple SVM classifiers and their outputs are combined for classifying the fault type and identifying the faulty section. Training and testing samples for the proposed method have been generated with different types of short-circuit faults and different combinations of system parameters in the MATLAB environment. The performance of the proposed method is investigated according to the accuracy of fault classification and faulty section identification. To evaluate the validity of this study, the proposed method is also compared to both ST--neural network and previous studies. The proposed method not only provides a good classification performance for all types of faults, but also detects the faulty section at a high accuracy.
A combined protective scheme for fault classification and identification of faulty section in series compensated transmission lines
The fault detection process is very difficult in transmission lines with a fixed series capacitor because of the nonlinear behavior of protection device and series-parallel resonance. This paper proposes a new method based on S-transform (ST) and support vector machines (SVMs) for fault classification and identification of a faulty section in a transmission line with a fixed series capacitor placed at the middle of the line. In the proposed method, the fault detection process is carried out by using distinctive features of 3-line signals (line voltages and currents) and zero sequence current. The relevant features of these signals are obtained by using the ST. The obtained features are then used as input to multiple SVM classifiers and their outputs are combined for classifying the fault type and identifying the faulty section. Training and testing samples for the proposed method have been generated with different types of short-circuit faults and different combinations of system parameters in the MATLAB environment. The performance of the proposed method is investigated according to the accuracy of fault classification and faulty section identification. To evaluate the validity of this study, the proposed method is also compared to both ST--neural network and previous studies. The proposed method not only provides a good classification performance for all types of faults, but also detects the faulty section at a high accuracy.
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- J. Woodworth, “MOV protection of series capacitor banks”, Arrester Factors008, 2008. Available at http://arresterworks.com/ArresterFacts files/ArresterFacts%20008%20-%20MOV%20Protection%20of%20Series%20 Capacitor%20Banks.pdf.
- A.K. Pradhan, A. Routray, S. Pati, D.K. Pradhan, “Wavelet-fuzzy combined approach for fault classification of a series-compensated transmission line”, IEEE Transactions on Power Delivery, Vol. 19, pp. 1612–1618, 2005.
- U.B. Parikh, B. Das, R. Maheshwari, “Fault classification technique for series compensated transmission line using support vector machine”, International Journal of Electrical Power and Energy Systems, Vol. 32, pp. 629–636, 2010. O. ¨ Ozg¨ onenel, G. ¨ Onbilgin, C ¸ . Kocaman, “Transformer protection using the wavelet transform”, Turkish Journal of Electrical Engineering & Computer Sciences, Vol. 13, pp. 119–135, 2005.
- E. Kılı¸ c, O. ¨ Ozg¨ onenel, ¨ O. Usta, D. Thomas, “PCA based protection algorithm for transformer internal faults”, Turkish Journal of Electrical Engineering & Computer Sciences, Vol. 17, pp. 125–142, 2009.
- M.A. Khan, O. Ozgonenel, M.A. Rahman, “Wavelet transform based protection of stator faults in synchronous generators”, Electric Power Components and Systems, Vol. 35, pp. 625–637, 2007.
- O. Ozgonenel, E. Kilic, M.A. Khan, M.A. Rahman, “A new method for fault detection and identification of incipient faults in power transformers”, Electric Power Components and Systems, Vol. 36, pp. 1226–1244, 2008.
- U.B. Parikh, B. Das, R.P. Maheshwari, “Combined wavelet-SVM technique for fault zone detection in a series compensated transmission line”, Transactions on Power Delivery, Vol. 23, pp. 1789–1794, 2008.
- A.K. Pradhan, A. Routray, B. Biswal, “Higher order statistics-fuzzy integrated scheme for fault classi?cation of a series compensated line”, Transactions on Power Delivery, Vol. 19, pp. 891–893, 2004.
- Q.Y. Xaun, Y.H. Song, A.T. Johns, R. Morgan, D. Williams, “Performance of an adaptive protection scheme for series compensated EHV transmission systems using neural networks”, Electric Power System Research, Vol. 36, pp. 57–66, 1996.
- D. Novosel, B. Bachman, D. Hart, Y. Hu, M.M. Saha, “Algorithms for locating faults on series compensated lines using neural network and deterministic methods”, Transactions on Power Delivery, Vol. 11, pp. 1728–1736, 1996. Y.H. Song, A.T. Johns, Q.Y. Xuan, “Artificial neural network based protection scheme for controllable series compensated EHV transmission lines”, IEE Proceedings: Generation, Transmission, and Distribution, Vol. 143, pp. 535–540, 1996.
- Y.H. Song, Q.Y. Xuan, “Protection scheme for EHV transmission systems with thyristor controlled series compensation using radial basis function neural networks”, Electric Machines and Power Systems, Vol. 25, pp. 553–565, 19 P.K. Dash, A.K. Pradhan, G. Panda, “Application of minimal radial basis function neural network to distance protection”, Transactions on Power Delivery, Vol. 16, pp. 68–74, 2001.
- A.Y. Abdelaziz, A.M. Ibrahim, M.M. Mansour, H.E. Talaat, “Modern approaches for protection of series compensated transmission lines”, Electric Power Systems Research, Vol. 75, pp. 85–98, 2005.
- G. Feng, G.B. Huang, Q. Lin, R. Gay, “Error minimized extreme learning machine with growth of hidden nodes and incremental learning”, Transactions on Neural Networks, Vol. 20, pp. 1352–1357, 2009.
- U.B. Parikh, B.R. Bhalja, R.P. Maheshwari, B. Das, “Decision tree based fault classification scheme for protection of series compensated transmission lines”, International Journal of Emerging Electric Power Systems, Vol. 8, pp. 1–12, 2007.
- P.K. Dash, S.R. Samantaray, G. Panda, “Fault classi?cation and section identification of an advanced seriescompensated transmission line using support vector machine”, Transactions on Power Delivery, Vol. 22, pp. 67–73, 200 V. Malathi, N.S. Marimuthu, S. Baskar, “A comprehensive evaluation of multicategory classification methods for fault classification in series compensated transmission line”, Neural Computing and Applications, Vol. 19, pp. 595–600, 2009.
- A.I. Megahed, A.M. Moussa, A.E. Bayoumy, “Usage of wavelet transform in the protection of series-compensated transmission lines”, Transactions on Power Delivery, Vol. 21, pp. 1213–1221, 2006.
- M. Uyar, S. Yildirim, M.T. Gencoglu, “An expert system based on S-transform and neural network for automatic classification of power quality disturbances”, Expert Systems with Applications, Vol. 36, pp. 5962–5975, 2009.
- M.J. Dehghani, “Comparison of S-transform and wavelet transform in power quality analysis”, World Academy of Science, Engineering and Technology, Vol. 50, pp. 395–398, 2009.
- R.G. Stockwell, L. Mansinha, R.P. Lowe, “Localization of the complex spectrum: the S-transform”, Transactions on Signal Processing, Vol. 44, pp. 998–1001, 1996.
- M.V. Chilukuri, P.K. Dash, “Multiresolution S-transform-based fuzzy recognition system for power quality events”, Transactions on Power Delivery, Vol. 19, pp. 323–330, 2004.
- M. Uyar, S. Yıldırım, M. T. Gen¸ co˘ glu, “G¨ u¸ c kalitesindeki bozulma t¨ urlerinin sınıflandırılması i¸ cin bir ¨ or¨ unt¨ u tanıma yakla¸sımı”, Gazi ¨ Universitesi M¨ uh. Mim. Fak. Dergisi, Vol. 26, pp. 41–56, 2011 (article in Turkish).
- S.R. Samantaray, P.K. Dash, “Pattern recognition based digital relaying for advanced series compensated line”, International Journal of Electrical Power and Energy System, Vol. 30, pp. 102–112, 2008.
- Z. Moravej, M. Pazoki, A.A. Abdoos, “A new approach for fault classification and section detection in compensated transmission line with TCSC”, European Transactions on Electrical Power, Vol. 21, pp. 997–1014, 2011.
- H. Shu, X. Tian, P. Cao, C. Liu, “Fault classification and location of power transmission lines with S-transform and artificial neural network”, International Conference on Energy Systems and Electrical Power, Energy Procedia, Vol. 13, pp. 5991–5998, 2011.
- H. Eri¸sti, Y. Demir, “A new algorithm for automatic classification of power quality events based on wavelet transform and SVM”, Expert Systems with Applications, Vol. 37, pp. 4094–4102, 2010.
- S.S. Sahu, G. Panda, N.V. George, “An improved S-transform for time-frequency analysis”, International Advance Computing Conference, pp. 315–319, 2009.
- Y.H. Wang, “The tutorial: S-transform lecture note”, Graduate Institute of Communication Engineering, National Taiwan University. S. Ekici, “Classification of power system disturbances using support vector machines”, Expert Systems with Applications, Vol. 36, pp. 9859–9868, 2009.
- B. Ravikumar, D. Thukaram, H.P. Khincha, “Comparison of multiclass SVM classification methods to use in a supportive system for distance relay coordination”, Transactions on Power Delivery, Vol. 25, pp. 1296–1305, 2010. V.N. Vapnik, Statistical Learning Theory, New York, Wiley, 1998.
- J. Weston, J.C. Watkins, “Multi-class support vector machines”, Technical Report, Department of Computer Science, University of London, 1998.
- S.S. Keerthi, C.J. Lin, “Asymptotic behaviors of support vector machines with Gaussian kernel”, Neural Computation, Vol. 15, pp. 667–1689, 2003.
- B. Sch¨ olkopf, K.K. Sung, C.J. Burges, F. Girosi, P. Niyogi, T. Poggio, V. Vapnik, “Comparing support vector machines with Gaussian kernels to radial basis function classifiers”, Transactions on Signal Processing, Vol. 45, pp. 2758–2765, 1997.