A Simultaneous Perturbation Stochastic Approximation (SPSA)-Based Model Approximation and its Application for Power System Stabilizers

  • Ko, Hee-Sang (Samsung Heavy Industries Co.) ;
  • Lee, Kwang-Y. (Department of Electrical & Computer Engineering, Baylor University) ;
  • Kim, Ho-Chan (Department of Electrical Engineering, Cheju National University)
  • Published : 2008.08.31

Abstract

This paper presents an intelligent model; named as free model, approach for a closed-loop system identification using input and output data and its application to design a power system stabilizer (PSS). The free model concept is introduced as an alternative intelligent system technique to design a controller for such dynamic system, which is complex, difficult to know, or unknown, with input and output data only, and it does not require the detail knowledge of mathematical model for the system. In the free model, the data used has incremental forms using backward difference operators. The parameters of the free model can be obtained by simultaneous perturbation stochastic approximation (SPSA) method. A linear transformation is introduced to convert the free model into a linear model so that a conventional linear controller design method can be applied. In this paper, the feasibility of the proposed method is demonstrated in a one-machine infinite bus power system. The linear quadratic regulator (LQR) method is applied to the free model to design a PSS for the system, and compared with the conventional PSS. The proposed SPSA-based LQR controller is robust in different loading conditions and system failures such as the outage of a major transmission line or a three phase to ground fault which causes the change of the system structure.

Keywords

References

  1. F. P. deMello and C. A. Concordia, "Concept of synchronous machine stability as affected by excitation control," IEEE Trans. on Power Apparatus and Systems, vol. 103, pp. 316-319, 1969
  2. S. S. Lee, S. H. Kang, G. S. Jang, S. Y. Li, J. K. Park, S. I. Moon, and Y. T. Yoon, "Damping analysis using IEEEST PSS and PSS2A PSS," Journal of Electrical Engineering & Technology, vol. 1, no. 3, pp. 271-278, 2006 https://doi.org/10.5370/JEET.2006.1.3.271
  3. S. A. Doi, "Coordinated synthesis of power system stabilizers in multimachine power systems," IEEE Trans. on Power Apparatus and Systems, vol. 103, pp. 1473-1479, 1984 https://doi.org/10.1109/TPAS.1984.318486
  4. T. L. Hwang, T. Y. Hwang, and W. T. Yang, "Two-level optimal output feedback stabilizer design," IEEE Trans. on Power Systems, vol. 6, no. 3, pp.1042-1047, 1991 https://doi.org/10.1109/59.119244
  5. K. T. Law, D. J. Hill, and N. R. Godfrey, "Robust controller structure for coordinate power system voltage regulator and stabilizer design," IEEE Trans. Control Sys. Tech., vol. 2, no. 3, pp. 220-232, 1994 https://doi.org/10.1109/87.317979
  6. A. Ghosh, G. Ledwich, O. P. Malik, and G. S. Hope, "Power system stabilizer based on adaptive control techniques," IEEE Trans. on Power Apparatus and Systems, vol. 103, pp. 1983-1989, 1984 https://doi.org/10.1109/TPAS.1984.318503
  7. W. Gu and K. E. Bollinger, "A self-tuning power system stabilizer for wide-range synchronous generator operation," IEEE Trans. on Power Systems, vol. 4, no. 2, pp. 1191-1199, 1989 https://doi.org/10.1109/59.32617
  8. O. P. Malik and C. Mao, "An adaptive optimal controller and its application to an electric generating unit," Int. J. Electr. Power Energy Generating Unit, vol. 15, pp. 169-178, 1993 https://doi.org/10.1016/0142-0615(93)90032-I
  9. Y. Zhang, O. P. Malik, G. S. Hope, and G. P. Chen, "Application of an inverse input/output mapped ANN as a power system stabilizer," IEEE Trans. on Energy Conversion, vol. 9, no. 3, pp. 433-441, 1994 https://doi.org/10.1109/60.326460
  10. K. Y. Lee and H. S. Ko, "Power system stabilization using free-model based inverse dynamic neuro controller," Proc. of Int. Joint Conf. Neural Network, no. 3, pp. 2132-2137, 2002
  11. K. A. El-Metwally and O. P. Malik, "Fuzzy logic power system stabilizer," IEE Proc. Generation Transmission Distribution, vol. 143, no. 3, pp. 263-268, 1996
  12. Y. Y. Hsu and C. H. Cheng, "Design of fuzzy power system stabilizers for multi-machine power systems," IEE Proc. Generation Transmission Distribution, vol. 137, no. 3, pp. 233-238, 1990
  13. R. A. Hooshmand and M. Ataei, "Real-coded genetic algorithm based design and analysis of an auto-tuning fuzzy logic PSS," Journal of Electrical Engineering & Technology, vol. 2, no. 2, pp. 178-187, 2007 https://doi.org/10.5370/JEET.2007.2.2.178
  14. J. C. Spall, "Multivariate stochastic approximation using a simultaneous perturbation gradient approximation," IEEE Trans. on Automatic Control, vol. 37, pp. 332-341, 1992 https://doi.org/10.1109/9.119632
  15. J. C. Spall, Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control, Wiley, 2003
  16. B. D. O. Anderson and J. B. More, Linear Optimal Control, Prentice Hall, New Jersey, 1990
  17. P. W. Sauer and M. A. Pai, Power System Dynamics and Stability, Prentice Hall, New Jersey, 1998
  18. P. M. Anderson and A. A. Fouad, Power Systems Control and stability, Iowa State University Press, USA, 1984
  19. C. L. Phillips and H. T. Nagle, Digital Control System Analysis and Design, Prentice Hall, 1997
  20. K. Ogata, Discrete-Time Control System, Prentice Hall, 1995
  21. M. A. Pai, C. D. Vournas, A. N. Michel, and H. Ye, "Application of interval matrices in power system stabilizer design," Int. J. Elec. Power Energy Syst., vol. 19, no. 3, pp. 179-184, 1997 https://doi.org/10.1016/S0142-0615(96)00041-5