Abstract
This paper proposes a novel method Modified Hilbert Huang Transform which is the combination of empirical-mode decomposition (EMD) and Hilbert transform with an equivalent window for Time frequency analysis. Initially the Non-stationary power signal is decomposed using EMD to get Intrinsic Mode functions (IMFs) and then Hilbert transform with an equivalent window is applied to all the IMFs to obtain instantaneous amplitude and frequency for Modified Hilbert Energy Spectrum. Different features are extracted from the Modified Hilbert Energy Spectrum and these features are applied to the Bacteria Foraging algorithm for automatic classification.
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Jagadeesh, S., Biswal, B. (2015). Time Frequency Analysis and Classification of Power Quality Events Using Bacteria Foraging Algorithm. In: Jain, L., Behera, H., Mandal, J., Mohapatra, D. (eds) Computational Intelligence in Data Mining - Volume 3. Smart Innovation, Systems and Technologies, vol 33. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2202-6_31
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DOI: https://doi.org/10.1007/978-81-322-2202-6_31
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