S-Transform-Based Classification of Converter Faults in HVDC System by Support Vector Machines

Abstract:

Based on Support Vector Machines (SVM) and S-transform, a novel approach to detect and classify various types of high voltage direct current (HVDC) converter faults is presented. An electro-magnetic transient state simulation software PSCAD/EMTDC was used to set up a simulation model of HVDC system to investigate the typical converter faults. For the good time-frequency characteristic of S-transform, S-transform is applied to obtain useful features of the non-stationary fault signals. Then fault types are identified through the pattern recognition classifier based on SVM classification tree. Numerical results show that the proposed classification method is an effective technique for building up a pattern recognition system for converter fault signals.

• Periodical: Applied Mechanics and Materials (Volumes 347-350)
• Pages: 1308-1312
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.347-350.1308
• Online since: August 2013
• Authors: Si Zhuo Lv, Jun Wen, Si Yu Zhou, Wen Jia Cao
• Keywords: Classification Tree, Fault Classification, HVDC Converter, S-Transform, Support Vector Machine (SVM)

References

[1] G. Mazur, R. Carryer, S. T. Ranade and T. Wess, Converter control and protection of Nelson river HVDC bipolar commissioning and first year of commercial operation, IEEE Trans. Power App. Syst., vol. PAS-100, no. 5, pp.327-335, Jan. (1981).

DOI: 10.1109/tpas.1981.316860

[2] C. V. Thio, J. B. Davies, and K. L. Kent. Commutation failures in HVDC transmission systems, IEEE Trans. Power Del., vol 11, no. 2, pp.946-957, Apr. (1996).

DOI: 10.1109/61.489356

[3] A. Hansen, and H. Havemann, Decreasing the commutation failure frequency in HVDC transmission systems, IEEE Trans. Power Del., vol 15, no. 3, pp.1022-1026, Jul. (2000).

DOI: 10.1109/61.871369

[4] S. Ventosa, C. Simon, Martin Schimmel, J. J. Danobeitia, and A. Mànuel, The S-transform from a wavelet point of view, IEEE Trans. Signal Process., vol. 56, no. 7, p.2771–2780, Jul. (2008).

DOI: 10.1109/tsp.2008.917029

[5] I. W. C. Lee, P. K. Dash, S-transform-based intelligent system for classification of power quality disturbance signals, IEEE Trans. Ind. Electron., vol. 50, no. 4, p.800–805, Aug. (2003).

DOI: 10.1109/tie.2003.814991

[6] M. V. Chilukuri, P. K. Dash, Multiresolution S-transform-based fuzzy recognition system for power quality events, IEEE Trans. Power Del., vol. 19, no. 1, p.323–330, Jan. (2004).

DOI: 10.1109/tpwrd.2003.820180

[7] S. R. Samantarary, B. K. Panigrahi, P. K. Dash and G. Panda, Power transformer protection using S-transform with complex window and pattern recognition approach, IET Gener. Transm. Distrib., vol. 19, no. 1, p.278–286, Jan. (2007).

DOI: 10.1049/iet-gtd:20060206

[8] N. Kandil, V. K. Sood, K. Khorasani, and R. V. Patel, Fault identification in an ac-dc transmission system using neural networks, IEEE Trans. Power Syst., vol. 7, No. 2, pp, 812-819. May. (1992).

DOI: 10.1109/59.141790

[9] Youshen Xia, and Jun Wang, A One-Layer Recurrent Neural Network for Support Vector Machine Learning, IEEE Trans. Syst., Vol. 34, No. 2, pp.1261-1269, Apr (2004).

DOI: 10.1109/tsmcb.2003.822955

[10] W. M. Lin, C. H. Wu, C. H. Lin, and F.S. Cheng, Detection and classification of multiple power-quality disturbances with wavelet multiclass SVM, IEEE Trans. Power Del., Vol. 23, No. 4, pp.2575-2582, Oct. (2008).

DOI: 10.1109/tpwrd.2008.923463

[11] P. Janik and T. Lobos, Automated classification of power-quality disturbances using SVM and RBF Networks, IEEE Trans. Power Del., Vol. 21, No. 3, pp.1663-1669, Jul. (2006).

DOI: 10.1109/tpwrd.2006.874114