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Power System Dynamic State Estimation based on Kalman Filter

by Firas M. Taimah, Akram N. Merzah
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 154 - Number 11
Year of Publication: 2016
Authors: Firas M. Taimah, Akram N. Merzah
10.5120/ijca2016911935

Firas M. Taimah, Akram N. Merzah . Power System Dynamic State Estimation based on Kalman Filter. International Journal of Computer Applications. 154, 11 ( Nov 2016), 26-30. DOI=10.5120/ijca2016911935

@article{ 10.5120/ijca2016911935,
author = { Firas M. Taimah, Akram N. Merzah },
title = { Power System Dynamic State Estimation based on Kalman Filter },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2016 },
volume = { 154 },
number = { 11 },
month = { Nov },
year = { 2016 },
issn = { 0975-8887 },
pages = { 26-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume154/number11/26536-2016911935/ },
doi = { 10.5120/ijca2016911935 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:59:59.519606+05:30
%A Firas M. Taimah
%A Akram N. Merzah
%T Power System Dynamic State Estimation based on Kalman Filter
%J International Journal of Computer Applications
%@ 0975-8887
%V 154
%N 11
%P 26-30
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presented the application of Kalman Filtering technique in estimating the dynamic variables for the multi-machine power systems. The Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are both appropriate tools to be applied in power system dynamic state estimation studies. EKF and UKF are implemented using a second-order swing equation and a classical generator model to estimate the dynamic state (generator rotor angle and generator rotor speed) and comparing the result which obtained from the two estimation algorithm (EKF and UKF) with the result from the fourth order Runge-Kutta method in order to show the statistical performance and estimation accuracy of each algorithm. The algorithms are mathematically demonstrated using the “IEEE 14-bus test system. The results show that the UKF method gives an accurate performance in the dynamic state estimation for multi-machine power system than the EKFmethod. It gives minimum mismatch between estimated state and actual state.

References
  1. Fred. C. Schweppe, J. Wildes, and Douglas. B. Rom, "Power system static-state estimation, part I, II and III", IEEE Transactions on Power Apparatus and Systems, vol. PAS-89, NO. 1, pp. 120-135, 1970.
  2. Ankhi Gulati , Niyati Gulati and Sushil Kr. Solanki, " Overview of State Estimation Technique for Power System Control", IOSR Journal of Electrical and Electronics Engineering, vol.8, pp.51-55, Nov. - Dec. 2013.
  3. F. Schweppe, “Power system static-state estimation, part III: Implementation”, IEEE Trans. Power App. Syst., vol. PAS-89, no. 1, pp. 130–135, 1970.
  4. Sideig A. Dowi,and Amar Ibrahim Hamza, “Dynamic State Forecasting in Electric Power Networks”, Journal of Power and Energy Engineering, vol. 2, pp. 1-11, 2014.
  5. Omprakash s. mankar and K. Vadirajacharya, “ Kalman filterI anlysis in dynamic state of power system”. International Research Journal of Engineering and Technology , vol. 2, pp.1170-1173, 2015.
  6. A. S. Debs and R. E. Larson, “A Dynamic Estimator for Tracking the State of a Power System”, IEEE Transactions on Power Apparatus and Systems, vol. PAS-89, no. 7, pp. 1670–1678, 1970.
  7. Amit Jain and Shivakumar N. R., “Power System Tracking and Dynamic State Estimation“, In IEEE PES Power Systems Conference Exposition (PSCE) 2009.
  8. K. P. Schneider, Z. Huang, B. Yang, M. Hauer, and Y. Nieplocha, “Dynamic state estimation utilizing high performance computing methods,” Power Systems Conference and Exposition, 2009. PSCE ’09. IEEE/PES. pp. 1–6, 2009.
  9. C. Bila, “Power system dynamic state estimation and load modeling”, M.S. thesis, Dept. Electrical and Computer Eng., Northeastern Univ., Boston, Massachusetts, 2014.
  10. D. Simon, “Optimal State Estimation”, Wiley-Interscience, Jone Wiley and Sons, Inc., Hoboken, New Jersey, 2006.
  11. G. Valverde and V. Terzija, “Unscented Kalman filter for power system dynamic state estimation”, IET Generation, Transmission and Distribution, vol.5, Iss. 1, pp. 29-37, July 2010.
  12. Huang, Li and Yan W., “Estimating parameters of synchronous generators using square-root unscented Kalman filter”, Electric Power Syst. Res., vol. 8, no. 9, pp. 1137–1144, 2010.
  13. Sarkka, S.: “On unscented Kalman filtering for state estimation of continuous-time nonlinear systems”, IEEE Trans. Autom. Control , vol.52, (9), pp. 1631–1641, 2007.
  14. D. Simon, “Optimal state estimation: Kalman, H infinity, and nonlinear approaches”, Wiley, New Jersey, 2006.
  15. Dabbagchi, I.and Christie R., “Power system test case archive”, (Resources at Electrical Engineering, The University of Washington, 1993).
Index Terms

Computer Science
Information Sciences

Keywords

Dynamic State Estimation (DSE) Kalman Filter (KF) Extended Kalman Filter (EKF) Unscented Kalman Filter (UKF)

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