Frequency and parameter estimation of multi-sinusoidal signal

Petrović, P.

doi:10.2478/v10048-012-0027-4

Abstract

Estimating the fundamental frequency and harmonic parameters is basic for signal modeling in a power supply system. This paper presents a complexity-reduced algorithm for signal reconstruction in the time domain from irregularly spaced sampling values. Differing from the existing parameter estimation algorithms, either in power quality monitoring or in harmonic compensation, the proposed algorithm enables a simultaneous estimation of the fundamental frequency, the amplitudes and phases of harmonic waves. The reduction in complexity is achieved owing to completely new analytical and summarized expressions that enable a quick estimation at a low numerical error. It is proved that the estimation performance of the proposed algorithm can attain Cramer-Rao lower bound (CRLB) for sufficiently high signal-to-noise ratios. The proposed algorithm can be applied in signal reconstruction, spectral estimation, system identification, as well as in other important signal processing problems. The simulation and experimental results verify the effectiveness of the proposed algorithm.

References

[1] Wang, F., Bollen, M. (2004). Frequency response characteristics and error estimation in RMS measurement. 1569-1578.10.1109/TPWRD.2004.835280
Search in Google Scholar
[2] Wand, M., Sun, Y. (2004). A practical, precise method for frequency tracking and phasor estimation. Trans. Power Delivery
Search in Google Scholar
[3] Agrež, D. (2010). Estimation and tracking of the power quality disturbances in the frequency domain. Meas. Sci. Rev., 10.2478/v10048-010-0032-4
Search in Google Scholar
[4] Terzija, V.V. (2003). Improved recursive Newton-type algorithms for frequency and spectra estimation in power systems. 1654-1659.10.1109/TIM.2003.817152
Search in Google Scholar
[5] Sidhu, T.T. (1999). Accurate measurement of power system frequency using a digital signal processing technique. 10.1109/19.755064
Search in Google Scholar
[6] Arpaia, P., Cruz Serra, A., Daponte, P., Monteiro, C.L. (2001). A critical note to IEEE 1057-94 standard on hysteretic ADC dynamic testing. Meas.10.1109/19.948304
Search in Google Scholar
[7] Wu, J., Long, J., Wang, J. (2005). High-accuracy, wide-range frequency estimation methods for power system signals under nonsinusoidal conditions. Trans. Power Delivery
Search in Google Scholar
[8] So, H.C., Chan, K.W., Chan, Y.T., Ho, K.C. (2005). Linear prediction approach for efficient frequency estimation of multiple real sinusoids: Algorithms and analyses. 2290-2305.10.1109/TSP.2005.849154
Search in Google Scholar
[9] Klein, J.D. (2006). Fast algorithms for single frequency estimation. (5), 1762-1770.10.1109/TSP.2006.870549
Search in Google Scholar
[10] El-Shafey, M.H., Mansour, M.M. (2006). Application of a new frequency estimation technique to power systems. 1045-1053.10.1109/TPWRD.2005.860246
Search in Google Scholar
[11] Trapero, J.R., Sira-Ramirez, H., Batlle, V.F. (2007). An algebraic frequency estimator for a biased and noisy sinusoidal signal. 1188-1201.10.1016/j.sigpro.2006.10.006
Search in Google Scholar
[12] Martens, O., Trampark, H., Liimets, A., Nobel, P., Veskimester, A., Jarvalt, A. (2008). DSP-based power quality monitoring device. In Symposium on Intelligent Signal Processing(WISP 2007)10.1109/WISP.2007.4447591
Search in Google Scholar
[13] Hindersah, H., Purwadi, A., Ali, F.Y., Heryana, N. (2011). Prototype development of single phase prepaid KWh meter. In Engineering and Informatics 1-6.10.1109/ICEEI.2011.6021692
Search in Google Scholar
[14] Petrovic, P. (2010). Fourier coefficient estimation based on the differential values of the processed signal. 10.4103/0377-2063.61262
Search in Google Scholar
[15] Poberezhskiy, Y.S., Poberezhskiy, G.Y. (2004). Sampling and signal reconstruction circuits performing internal antialiasing filtering and their influence on the design of digital receivers and transmitters. Trans. Circ. Syst.-I
Search in Google Scholar
[16] Cooklev, T. (2006). An efficient architecture for orthogonal wavlet transforms. Lett.
Search in Google Scholar
[17] Schoukens, J., Rolain, Y., Simon, G., Pintelon, R. (2003). Fully automated spectral analysis of periodic signals. 1021-1024.10.1109/TIM.2003.814817
Search in Google Scholar
[18] Reeves, S.J. (1999). An efficient implementation of the backward greedy algorithm for sparse signal reconstruction. 266-268.10.1109/97.789606
Search in Google Scholar
[19] Kay, S.M. (1988). Modern Spectral Estimation:Theory and Applications. Prentice-Hall.
Search in Google Scholar
[20] Stoica, P., Li, H., Li, J. (2000). Amplitude estimation of sinusoidal signals: Survey, new results, and an application. 338-352.10.1109/78.823962
Search in Google Scholar
[21] Belega, D., Dallet, D., Slepicka, D. (2010). Accurate amplitude estimation of harmonic components of incoherently sampled signals in the frequency domain. IEEE Trans. Instrum. Meas., 10.1109/TIM.2010.2045144
Search in Google Scholar
[22] Pantazis, Y., Roces, O., Stylianou, Y. (2010). Iterative estimation of sinusoidal signal parameters. Signal Process. Lett.10.1109/LSP.2010.2043153
Search in Google Scholar
[23] Hidalgo, R.M., Fernandez, J.G., Rivera, R.R., Larrondo, H.A. (1996). A simple adjustable window algorithm to improve FFT measurements. Instrum. Meas.
Search in Google Scholar
[24] Agrež, D. (2002). Weighted multi-point interpolated DFT to improve amplitude estimation of multifrequency signal. 287-292.
Search in Google Scholar
[25] Agrež, D. (2005). Improving phase estimation with leakage minimization. (4), 1347-1353.10.1109/TIM.2005.851058
Search in Google Scholar
[26] Tse, N.C.F., Lai, L.L. (2007). Wavelet-based algorithm for signal analysis. Advances in Signal Processing 38916.10.1155/2007/38916
Search in Google Scholar

Frequency and parameter estimation of multi-sinusoidal signal

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