Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지
DOI QR코드

DOI QR Code

Hybrid PSO-Complex Algorithm Based Parameter Identification for a Composite Load Model

  • Del Castillo, Manuelito Y. Jr. (Dept. of Electrical Engineering, Seoul Nat'l University of Science and Technology) ;
  • Song, Hwachang (Dept. of Electrical and Inform. Eng., Seoul Nat'l Univ. of Science and Tech.) ;
  • Lee, Byongjun (School of Electrical Engineering, Korea University)
  • Received : 2012.06.25
  • Accepted : 2012.12.23
  • Published : 2013.05.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes a hybrid searching algorithm based on parameter identification for power system load models. Hybrid searching was performed by the combination of particle swarm optimization (PSO) and a complex method, which enhances the convergence of solutions closer to minima and takes advantage of global searching with PSO. In this paper, the load model of interest is composed of a ZIP model and a third-order model for induction motors for stability analysis, and parameter sets are obtained that best-fit the output measurement data using the hybrid search. The origin of the hybrid method is to further apply the complex method as a local search for finding better solutions using the selected particles from the performed PSO procedure.

Keywords

References

  1. P. Kundur, Power system stability and control: McGraw Hill, 1994.
  2. L. Ljung, System identification: theory for the user: Prentice-Hall, 1999.
  3. T. Van Cutsem, C. Vournas, Voltage stability of electric power systems: Kluwer Academic Publishers, 1998.
  4. IEEE Task Force on Load Representation for Dynamic Performance, "Standard load models for power flow and dynamic performance simulation," IEEE Trans. on Power Systems, Vol. 10, No. 3, pp. 1302-1313, Aug. 1995. https://doi.org/10.1109/59.466523
  5. D. J. Hill, "Nonlinear dynamic load models with recovery for voltage stability studies," IEEE Trans. on Power Systems, Vol. 8, No. 1, pp. 166-172, Feb. 1993. https://doi.org/10.1109/59.221270
  6. W. Xu and Y. Masour, "Voltage stability analysis using generic dynamic load models," IEEE Trans. Power Systems, Vol. 9, No. 1, pp. 479-486, Feb. 1994. https://doi.org/10.1109/59.317575
  7. H.-D. Chiang, J.-C. Wang, C.-T. Huang, Y.-T. Chen, C.-H. Huang, "Development of a dynamic ZIP-motor load model from on-line field measurements," Int'l Journal of Electrical Machine & Energy Systems, Vol. 19, No. 7, pp. 459-468, Oct. 1997. https://doi.org/10.1016/S0142-0615(97)00016-1
  8. B. C. Lesieutre, P. W. Sauer, and M. A. Pai, "Development and comparative study of induction machine based dynamic P, Q load models," IEEE Trans. Power Systems, Vol. 10, No. 1, pp. 182-191, Feb. 1995. https://doi.org/10.1109/59.373941
  9. A. Ellis, D. Kosterev, A. Meklin, "Dynamic load models: Where are we?," in Proceedings of The IEEE 2005/2006 Transmission and Distribution Conference and Exhibition, Dallas, USA, May 2006.
  10. H. Bai, P. Zhang, V. Ajjarapu, "A novel parameter identification approach via hybrid learning for aggregate load modeling," IEEE Trans. on Power Systems, Vol. 24, No. 3, pp. 1145-1154, Aug. 2009.
  11. J. Ma, D. Han, R.-M. He, Z.-Y. Dong, D. Hill, "Reducing identified parameters of measurementbased composite load model," IEEE Trans. Power Systems, Vol. 23, No. 1, pp. 76-83, Feb. 2008. https://doi.org/10.1109/TPWRS.2007.913206
  12. D. Karlsson, D. J. Hill, "Modelling and identification of nonlinear dynamic loads in power systems," IEEE Trans. on Power Systems, Vol. 9, No. 1, pp. 157-163, Feb. 1994. https://doi.org/10.1109/59.317546
  13. C. Rehtanz, "Wide area protection and online stability assessment based on Phasor Measurement Units," in Proceedings of The 5th Conference on POWER Systems Dynamics and Control, Onomichi, Japan, Aug. 2001.
  14. R. Eberhart and J. Kennedy, "A new optimizer using particle swarm theory," in Proceedings of The 6th International Symposium on Micro Machine and Human Science, Nagoya, Japan, Oct. 1995.
  15. R. Eberhart, Y. Shi, "Particle swarm optimization: developments, applications and resources," in Proceedings of The 2001 congress on Evolutionary Computation, Seoul, Korea, May 2001.
  16. A. Ide and K. Yasuda, "A basic study of adaptive particle swarm optimization," Electrical Engineering in Japan, Vol. 151, No. 3, pp. 41-49, March 2005. https://doi.org/10.1002/eej.20077
  17. R. Kazmierczak, Jr., Optimizing Complex Bioeconomic Simulations Using Efficient Search Heuristic, DAEUSA Research Report No. 704, 1996.
  18. Z. Pei, S. Tian, H. Huang, "A novel method for solving non-linear bi-level programming based on hybrid particle swarm optimization," in Proceedings of The 8th International Conference on Signal Processing, Beijing, China, Nov. 2006.
  19. S. Fan, E. Zahara, "A hybyid simplex search and particle swarm optimization for unconstrained optimization," European Journal of Operational Research, Vol. 181, No. 2, pp. 527-548, Sept. 2007. https://doi.org/10.1016/j.ejor.2006.06.034
  20. P. Zang, P. Wei, H. Yu, Z. Wang, "Simplex particle swarm optimization for block matching algorithm," in Proceedings of The International Symposium on Intelligent Signal Processing and Communications Systems, Chengdu, China, Dec., 2010.
  21. Y.G. Kim, H. Song, H. R. Kim, B. Lee, "Particle swarm optimization based load model parameter identification," in Proceedings of The 2010 IEEE General Meeting of Power and Energy Society, Minneapolis, USA, July 2010.
  22. H. Song, B. Lee, "Identification of dynamic load model parameters using a hybrid PSO-Simplex method," ICIC Express Letters, Vol. 5, No. 11, pp. 4021-4026, Nov. 2011.
  23. C. C. Hsu and C.H. Gao, "Particle swarm optimization simplex search and center particle for global optimization," in Proceedings of 2008 IEEE Conference on Soft Computing in Industrial Applications, Muroran, Japan, June 2008.

Cited by

  1. Fast and Reliable Estimation of Composite Load Model Parameters Using Analytical Similarity of Parameter Sensitivity vol.31, pp.1, 2016, https://doi.org/10.1109/TPWRS.2015.2409116
  2. Development of the automatic load modelling system using PQM data on industry site vol.7, pp.1, 2017, https://doi.org/10.1080/22348972.2016.1158350