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
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Real Time Dynamic State Estimation for Power System

by A. Thabet, M. Boutayeb, M.N. Abdelkrim
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 38 - Number 2
Year of Publication: 2012
Authors: A. Thabet, M. Boutayeb, M.N. Abdelkrim
10.5120/4659-6755

A. Thabet, M. Boutayeb, M.N. Abdelkrim . Real Time Dynamic State Estimation for Power System. International Journal of Computer Applications. 38, 2 ( January 2012), 11-18. DOI=10.5120/4659-6755

@article{ 10.5120/4659-6755,
author = { A. Thabet, M. Boutayeb, M.N. Abdelkrim },
title = { Real Time Dynamic State Estimation for Power System },
journal = { International Journal of Computer Applications },
issue_date = { January 2012 },
volume = { 38 },
number = { 2 },
month = { January },
year = { 2012 },
issn = { 0975-8887 },
pages = { 11-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume38/number2/4659-6755/ },
doi = { 10.5120/4659-6755 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:24:30.604505+05:30
%A A. Thabet
%A M. Boutayeb
%A M.N. Abdelkrim
%T Real Time Dynamic State Estimation for Power System
%J International Journal of Computer Applications
%@ 0975-8887
%V 38
%N 2
%P 11-18
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper investigates a method for the state estimation of nonlinear systems described by a class of differential-algebraic equation (DAE) models using the extended Kalman filter. The method involves the use of a transformation from a DAE to ordinary differential equation (ODE). A relevant dynamic power systems model using decoupled techniques will be proposed. The estimation technique consists of a state estimator based on the EKF technique as well as local stability analysis. High performances are illustrated through a real time application on 5 buses test system with DSP device (Dspace DS1104).

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. Neela R. and Aravindhababu P. 2009. A new decoupling strategy for power system state estimation. Int. J. of Energy Convers. and Management. 50. 2047–2051.
  3. 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.
  4. Scholtz E. 2004. Observer based monitors and distributed wave controllers for electromechanical disturbances in power systems. Doctoral Thesis, Massachusetts Institute Technology, USA.
  5. 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.
  6. Da?s C.J. 2005. An observbility formulation for nonlinear power systems modeled as differential algebraic systems. Doctoral Thesis, Drexel university, PA, USA.
  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. Nikoukhah R., Willsky A. and Levy B. 1992. Kalman ?ltering and riccati equations for descriptor systems. IEEE Trans. on Autom. Control. 37, 1325–1342.
  11. 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.
  12. 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.
  13. Yu K., Watson N. and Arrillaga J. 2005. An adaptive kalman ?lter for dynamic harmonic state estimation and harmonic injection tracking. IEEE Trans. on Power Delivery. 20, 1577–1584.
  14. Ma H. and Grigis A. 1996. Identi?cation and tracking of harmonic sources in a power system using kalman ?lter. IEEE Trans. on Power Delivery. 11, 1659–1665.
  15. Beids H. and Heydt G. 1991. Dynamic state estimation of power system harmonic using kalman ?lter methodology. IEEE Trans. on Power Delivery. .6, 1663–1670.
  16. Chowdhury F., Christensen J. and Aravena J. 1991. Power system fault detection and state estimation using kalman ?lter with hypothesis testing. IEEE Trans. on Power Delivery. 6, 1025–1030.
  17. Roytelman I. and Shahidehpour S. 1993. State estimation for electric power distribution systems in quasi real-time conditions. IEEE Trans. on Power Delivery. 8, 2009–2015.
  18. Gordon B.W. 2003. Dynamic sliding manifolds for realization of high index differential-algebraic systems. Asian J. of Control., 5, 454–466.
  19. Tarraf D.C. and Asada H.H. 2002. On the nature and stability of differential-algebraic systems. In Proc. American Control Conf., Anchorage, Al USA, 3546–3551.
  20. Eremia M., Trecat J. and Germond A. 2000. Réseau électriques: Aspects Actuels, 2nd ed. Bucuresti: EdituraTehnica.
  21. Karlsson D. and Hill D.J. 1994. Modelling and identi?cation of nonlinear dynamic loads in power systems. IEEE Trans. on Power Syst. 9, 157–166.
  22. Judd K. 2003. Nonlinear state estimation ,indistinguishable states and the extended kalman ?lter. Physica D: Nonlinear Phenomena. 183, 273–281.
  23. Becerra V.M., Roberts P.D. and Grif?ths G.W. 2001. Applying the extended kalman ?lter to systems described by nonlinear differential-algebraic equations. Control Eng. Practice. 9, 267–281.
  24. Boutayeb M. and Aubry C. 1999. A strong tracking extended kalman observer for nonlinear discrete-time systems. IEEE Trans. on Autom. Control. 44, 1550–1556.
  25. Boutayeb M. 2000. Identi?cation of nonlinear systems in the presence of unknown but bounded disturbances. IEEE Trans. on Autom. Control. 45, 1503–1507.
  26. Song Y. and Grizzle J. 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 Extended Kalman Filter convergence analysis Time computing

Powered by PhDFocusTM