Abstract
The objective of this paper is to develop a method based on combination of empirical-mode decomposition (EMD) and Hilbert transform for power quality events classification. Non-stationary power signal disturbance waveform can be considered as superimposition of various undulating modes and EMD is used to separate out these intrinsic modes known as intrinsic mode functions (IMF). Hilbert transform is applied to all the IMF to achieve instantaneous amplitude and frequency. Relevant feature vectors are extracted to do the automatic classification. Time frequency analysis shows clear visual detection, localization and classification of the different power signal disturbances. A balanced neural tree is used to classify the power signal patterns.
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Jalaja, R., Biswal, B. (2013). Power Quality Event Classification Using Hilbert Huang Transform. In: Satapathy, S., Udgata, S., Biswal, B. (eds) Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA). Advances in Intelligent Systems and Computing, vol 199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35314-7_18
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DOI: https://doi.org/10.1007/978-3-642-35314-7_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35313-0
Online ISBN: 978-3-642-35314-7
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