Abstract
This paper presents a new power quality (PQ) disturbances identification method based on S-transform time-frequency analysis and RBF network. 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 events because of its superior performance under noise. Using the time-frequency characteristics, a set of features is extracted for identification of power quality disturbances. Finally, a RBF network is developed for classification of the power quality disturbances. The proposed method is simple and reached 97.5% identification correct ratio under high signal to noise ratio for those most important disturbances in power system.
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© 2006 Springer-Verlag Berlin Heidelberg
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Lv, G., Wang, X. (2006). Power Quality Identification Based on S-transform and RBF Neural Network. 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_52
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DOI: https://doi.org/10.1007/11816157_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
Online ISBN: 978-3-540-37273-8
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