Abstract
Support Vector Machine (SVM), which is based on Statistical Learning theory, is a universal machine learning method. This paper proposes the application of SVM in classifying the causes of voltage sag in power distribution system. Voltage sag is among the major power quality disturbances that can cause substantial loss of product and also can attribute to malfunctions, instabilities and shorter lifetime of the load. Voltage sag can be caused by fault in power system, starting of induction motor and transformer energizing. An IEEE 30 bus system is modeled using the PSCAD software to generate the data for different type of voltage sag namely, caused by fault and starting of induction motor. Feature extraction using the wavelet transformation for the SVM input has been performed prior to the classification of the voltage sag cause. Two kernels functions are used namely Radial Basis Function (RBF) and Polynomial function. The minimum and maximum of the wavelet energy are used as the input to the SVM and analysis on the performance of these two kernels are presented. In this paper, it has been found that the Polynomial kernel performed better as compared to the RBF in classifying the cause of voltage sag in power system.
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References
Bollen, M.H.J.: Voltage Sags in Three-Phase Systems. IEEE Power Engineering Review, 8–15 (2001)
Bollen, M.H.J.: Understanding Power Quality Problems, pp. 1–34. IEEE Press, Los Alamitos (2000)
McGranaghan, M.F., Mueller, D.R.: Voltage Sags in Industrial systems. IEEE Trans. On Industry Applications 29(2), 397–403 (1993)
IEEE Std. 1159-1995: Recommended Practice for Monitoring Electric Power Quality, ISBN 1-55937-549-3
Li, C., Tayjasanant, T., Xu, W., Li, X.: Method for voltage sag source detection by investigating slope of the system trajectory. IEE Proc. Gener. Transm. Distrib. 150(3), 367–372 (2003)
Axelberg, P.G.V., Gu, I.Y.-H., Bollen, M.H.J.: Support Vector Machine for Classification of Voltage Disturbances. IEEE Trans. On Power Delivery 22, 1297–1303 (2007)
Janik, P., Lobos, T.: Automated Classification of Power-Quality Disturbances UsingSVM and RBF Networks. IEEE Trans. On Power Delivery 21, 1663–1669 (2006)
Tong, W., Song, X., Lin, J., Zhao, Z.: Detection and Classification of Power Quality Disturbances Based on Wavelet Packet Decomposition and Support Vector Machines. In: Proc. Of the 8th Int. Conference on Signal Processing, vol. 4, pp. 16–20 (2006)
Lin, W.-M., Wu, C.-H., Lin, C.-H., Cheng, F.-S.: Classification of Multiple PowerQuality Disturbances Using Spport Vector Machine and One-versus-One Approach. In: Proc. of Int. Conference of Power system Technology, pp. 1–8 (2006)
Hu, G.-S., Zhu, F.-F., Ren, Z.: Power quality disturbance identification using wavelet packet energy entropy and weighted support vector machines (2007), http://www.sciencedirect.com
Xiong, S.-W., Niu, Z.-x., Liu, H.-B.: Support Vector Machines Based On Subtractive Clustering. In: Proc. Of the 4th Int. Conference on Machine Learning and Cybernetics, pp. 4345–4350 (2005)
Chen, S., Zhu, H.Y.: Wavelet Transform for Orocessing Power Quality Disturbances. EURASIP Journal on Advances in Signal Processing 2007
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Hamzah, N., Ismail, H., Zakaria, Z. (2009). The Application of Support Vector Machine in Classifying the Causes of Voltage Sag in Power System. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds) Rough Sets and Knowledge Technology. RSKT 2009. Lecture Notes in Computer Science(), vol 5589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02962-2_53
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DOI: https://doi.org/10.1007/978-3-642-02962-2_53
Publisher Name: Springer, Berlin, Heidelberg
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