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A comparative study on estimation techniques with applications to power signal frequency

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Języki publikacji
EN
Abstrakty
EN
An extended least square (ELS) technique has been proposed in this paper for power system frequency estimation. The validation of the above technique has been made by comparing its performance with the existing techniques such as Kalman filter (KF) and least mean square (LMS) technique etc. It has been observed through a series of simulation studies on frequency estimation that the ELS technique exhibits better performance in comparison to both the LMS and KF methods of power system frequency estimation. In Kalman filter, the determination of covariance matrix is very crucial leading to delay in convergence. LMS algorithm becomes complicated with the incorporation of correlation matrix, which may affect the convergence. On the contrary extended least square algorithm seems to be very simple and attractive without the implementation of covariance and correlation matrix. The feasibility of the ELS algorithm for frequency estimation has been tested with a signal buried with noise. The above estimation technique can be applied in real-time implementation, which will be immensely helpful for the power system protection. A comparative study on performance of the KF, LMS and ELS techniques for power system estimation has been made and included in the paper.
Rocznik
Strony
89--97
Opis fizyczny
Bibliogr. 10 poz., rys., tab., wzory
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autor
autor
autor
Bibliografia
  • [1] P. K. DASH, A. K. PRADHAN and G. PANDA: Frequency estimation of distorted power system signals using extended complex Kalman filter. IEEE Trans. Power Del., 14(3), (1999), 761-766.
  • [2] P. K. DASH, A. K. PRADHAN, G. PANDA, R. K. JENA and S. K. PANDA: On line tracking of varying harmonics using an integrated complex Kalman filter and Fourier linear combiner. Proc. IEEE Conf Power Engineering Society, 3 (2000), 1575-1580.
  • [3] A. K. PRADHAN, A. ROUTRAY and A. BASAK: Power system frequency estimation using least mean square technique. IEEE Trans. Power Del., 20(3), (2005), 1812-1816.
  • [4] M. BETTAYEB and U. QIDWAI: Recursive estimation of power system harmonics. Electric Power System Research, 47 (1998) 143-152.
  • [5] E. A. ABU AL-FEILAT, I. EL-AMIN and M. BETTAYEB: Power system harmonic estimation a comparative study. Electric Power System Research, 29 (1994), 91-97.
  • [6] M. S. SACHDEV and M. M. GIRAY: A least error square technique for determining power system frequency. IEEE Trans. Power Apparatus Syst., 104(2), (1985), 437-443.
  • [7] J. M. MENDEL: Lessons in digital estimation theory. Prentice-Hall, Englewood Cliffs, NJ, 1987.
  • [8] T. SODERSTROM and P. STOCIA: System identification. Prentice-Hall, Englewood Cliffs, NJ, 1989.
  • [9] L. LJUNG and T. SODERSTROM: Theory and practice of recursive identification. MIT Press, Cambridge, MA, 1983.
  • [10] L. JUNG: System identification - theory for the user. Prentice Hall, 1999.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW3-0045-0006
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