Abstract
This paper presents a new method based on S-transform time-frequency analysis and multi-lay SVMs classifier for identification of power quality (PQ) disturbances. The proposed technique consists of time-frequency analysis, feature extraction and pattern classification. Though there are several time-frequency analysis methods existing in the literature, this paper uses S-transform to obtain the time-frequency characteristics of PQ disturbances because of its superior performance under noise. With the time-frequency characteristics of ST result, a set of features is extracted for identification of PQ disturbances. Then PQ disturbances training samples were contrsucted with the features, and a multi-lay LS-SVMs classifier was trained by the training sample. Finally, the trained multi-lay LS-SVMs classifier was developed for classification of the PQ disturbances. The proposed method has an excellent performance on training speed and correct ratio. The correct ratio of identification could reach 98.3% and the training time of the N-1 classifier was only about 0.2s.
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References
Xu, Y.H., Xiao, X.N., Yang, Y.H., Chen, X.Y.: Power Quality Disturbance Identification Using Dq Conversion-based Neural Classifiers. Automation of Electric Power System 21, 24–28 (2001)
Sen, O.Y., Wang, J.H., Geng, Y.S., Song, Z.X.: Power Quality Classification Based on Wavelet Transform and Neural Network. Advanced Technology of Electrical Engineering and Energy 22, 32–36 (2003)
Dash, P.K., Mishra, S., Salama, M.M.A., Liew, A.C.: Classification of Power System Disturbances Using a Fuzzy Expert System and a Fourier Linear Combiner. IEEE Trans. Power Delivery 15, 472–477 (2000)
Heydt, G.T., Fjeld, P.S., Liu, C.C., Pierce, T.D.L., Hensley, G.: Applications of the Windowed FFT to Electric Power Quality Assessment. IEEE Trans. Power Delivery 14, 1411–1416 (1999)
Abdel-Galil, T.K., EL-Saadany, E.F., Youssef, A.M., Salama, M.M.A.: On-line Disturbance Recognition Utilizing Vector Quantization Based Fast Match. IEEE Power Engineering Society Summer Meeting 2, 1027–1032 (2002)
Styvaktakis, E., Bollen, M.H.J., Gu, I.Y.H.: Expert System for Classification and Analysis of Power System Events. IEEE Trans. Power Delivery 17, 423–428 (2002)
Gaouda, A.M., Kanoun, S.H., Salama, M.M.A.: On-line Disturbance Classification Using Nearest Neighbor Rule. J. Electr. Power Syst.Res 57, 1–8 (2001)
Lu, J.W., Plataniotis, K.N., Venetsanopoulos, A.N.: Face Recognition Using Feature Optimization and ν-support Vector Learning. In: Proceedings of the 2001 IEEE Signal Processing Society Workshop Neural Networks for Signal Processing XI, pp. 373–382 (2001)
Cortes, C., Vapnik, V.: Support-vector Networks. Machine Learning 20, 273–295 (1995)
Jack, L.B., Nandi, A.K.: Fault Detection Using Support Vector Machines and Artificial Neural Networks,Augmented by Genetic Algorithms. Mechanical Systems and Signal Processing 16, 373–390 (2002)
Francis, E.H., Tay, C.L.J.: Application of Support Vector Machines in Financial Time Series Forecasting. Omega 29, 309–317 (2001)
Chan, W.C., Cheung, K.C., Harris, C.J.: On the Modelling of Nonlinear Dynamic Systems Using Support Vector Neural Networks. Engineering Applications of Artificial Intelligence 14, 105–113 (2001)
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© 2006 Springer-Verlag Berlin Heidelberg
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Lv, G., Cai, X., Wang, X., Zhang, H. (2006). S-transform Based LS-SVM Recognition Method for Identification of PQ Disturbances. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_95
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DOI: https://doi.org/10.1007/11816157_95
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
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