Recursive Variational Mode Decomposition Algorithm for Real Time Power Signal Decomposition

Abstract Conventional methods of signal decomposition are observed to fail in power system applications and computationally intensive algorithms like EMD, VMD, EWT are found to give better performance. The heavy computations associated with them restricts their use in real time applications and stream processing. This paper presents a recursive block processing technique for real time signal decomposition. The use of recursive FFT and the clever initializations of the center frequencies in the existing VMD algorithm helps in reducing the computational complexity and hence speeds up the process. This low complexity algorithm was tested on synthetically generated power signals and the results were observed to be consistent with the existing VMD algorithm.

[1]  I. Daubechies,et al.  Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool , 2011 .

[2]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[3]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[4]  K. P. Soman,et al.  Performance Comparison of Variational Mode Decomposition over Empirical Wavelet Transform for the Classification of Power Quality Disturbances Using Support Vector Machine , 2015 .

[5]  J. Tukey,et al.  An algorithm for the machine calculation of complex Fourier series , 1965 .

[6]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

[7]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[8]  Jérôme Gilles,et al.  Empirical Wavelet Transform , 2013, IEEE Transactions on Signal Processing.

[9]  Paulo F. Ribeiro,et al.  Power Systems Signal Processing for Smart Grids , 2013 .

[10]  Mark J. Shensa,et al.  The discrete wavelet transform: wedding the a trous and Mallat algorithms , 1992, IEEE Trans. Signal Process..

[11]  Jacques Lemoine,et al.  Empirical mode decomposition: an analytical approach for sifting process , 2005, IEEE Signal Processing Letters.

[12]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.