We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

On the state estimation for Dynamic Power System

by A. Thabet, M. Boutayeb, M.n. Abdelkrim
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 3
Year of Publication: 2012
Authors: A. Thabet, M. Boutayeb, M.n. Abdelkrim
10.5120/5676-7712

A. Thabet, M. Boutayeb, M.n. Abdelkrim . On the state estimation for Dynamic Power System. International Journal of Computer Applications. 42, 3 ( March 2012), 45-52. DOI=10.5120/5676-7712

@article{ 10.5120/5676-7712,
author = { A. Thabet, M. Boutayeb, M.n. Abdelkrim },
title = { On the state estimation for Dynamic Power System },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 3 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 45-52 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number3/5676-7712/ },
doi = { 10.5120/5676-7712 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:30:34.381393+05:30
%A A. Thabet
%A M. Boutayeb
%A M.n. Abdelkrim
%T On the state estimation for Dynamic Power System
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 3
%P 45-52
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this contribution we provide a simple and useful state estimation approach for a general class of non linear models that describe dynamic power systems. At first we show, through a small power network, that this class of systems is modeled by non linear differential-algebraic equations that we may always transform to a system of ordinary differential equations. After, we investigate a state estimator based on the EKF technique as well as the local stability analysis. High performances are illustrated through a simulation study applied on 3 and 5 buses test systems.

References
  1. Shivakumar N. R. and Amit. 2008. A Review of Power System Dynamic State Estimation Techniques. In Power Syst. Technology and IEEE Power India Conf. , India. 1–6.
  2. Thabet A. et al. 2010. Power Systems Load Flow and State Estimation: Modified Methods and Evaluation of Stability and Speeds Computing. Int. Rev. of Electr. Eng. , 5, 1110–1118.
  3. Scholtz E. 2004. Observer based monitors and distributed wave controllers for electromechanical disturbances in power systems. Doctoral Thesis, Massachusetts Institute Technology, USA.
  4. Pai M. A. , Sauer P. W. , Lesieutre B. C and Adapa R. 1995. Structural stability in power systems effect of load models. IEEE Trans. on Power Syst. 10, 609–615.
  5. C. J. Da?s. 2005. An observbility formulation for nonlinear power systems modeled as differential algebraic systems. Ph. D. dissertation, Drexel university, PA, USA.
  6. Thabet A. , Boutayeb M. , Abdelkrim M. N. . 2012. Real time dynamic state estimation for power system. Int. J. of Computer Applications, 38, 11–18.
  7. Debes A. S. and Larson R. E. 1970. A dynamic estimator for tracking the state of a power system. IEEE Trans . on Power App. And Syst. 89, 1670–1678.
  8. Aslund J. and Frisk F. 2006. An observer for nonlinear differential-algebraic systems. Autmatica. 42, 959–965.
  9. Wichmann T. 2001. Simpli?cation of nonlinear DAE systems with index tacking. In European Conf. on Circuit Theory and Design, Espoo, Finland, 173–176.
  10. Isabel M. F. and Barbosa F. P. 1994. Square root ?lter algorithm for dynamic state estimation of electric power systems. In Electrotechnical Conference, 7th Mediterranean, Antalya, Turkey, 877–880.
  11. Shih K. and Huang S. 2002. Application of a robust algorithm for dynamic state estimation of a power system. IEEE Trans. on Power Syst. 17, 141–147.
  12. B. W. Gordon. 2003. Dynamic sliding manifolds for realization of high index differential-algebraic systems. Asian J. of Control. 5, 454–466.
  13. D. C. Tarraf and H. H. Asada. 2002. On the nature and stability of differential-algebraic systems. Proc. American Control Conf. , Anchorage, Al USA, 3546–3551.
  14. D. Karlsson and D. J. Hill. 1994. Modelling and identi?cation of nonlinear dynamic loads in power systems. IEEE Trans. on Power Syst. 9, 157–166.
  15. K. Judd. 2003. Nonlinear state estimation, indistinguishable states, and the extended kalman ?lter. Physica D: Nonlinear Phenomena. 183, 273–281.
  16. V. M. Becerra, P. D. Roberts, and G. W. Grif?ths. 2001. Applying the extended kalman ?lter to systems described by nonlinear differential-algebraic equations. Control Eng. Practice. 9, 267–281.
  17. M. Boutayeb and C. Aubry. 1999. A strong tracking extended kalman observer for nonlinear discrete-time systems. IEEE Trans. on Autom. Control. 44, 1550–1556.
  18. M. Boutayeb, (2000). Identi?cation of nonlinear systems in the presence of unknown but bounded disturbances. IEEE Trans. on Autom. Control. 45, 1503–1507.
  19. Y. Song and J. Grizzle. 1995. The extended kalman filter as a local asymptotic observer for nonlinear discrete time systems. J. Math. Syst. Estimation and Control. 5, 59–78.
Index Terms

Computer Science
Information Sciences

Keywords

Power System Dynamics State Estimation Extended Kalman Filter Convergence Analysis