Design of stabilising signals for power system damping using generalised predictive control optimised by a new hybrid shuffled frog leaping algorithm

Design of stabilising signals for power system damping using generalised predictive control optimised by a new hybrid shuffled frog leaping algorithm

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This study presents a hybrid method based on generalised predictive control (GPC) and a proposed new hybrid shuffled frog leaping (NHSFL) algorithm to design stabilising signals to damp the multi-machine power system low-frequency oscillations. A linearised model predictive controller based on GPC is designed in which the proposed NHSFL algorithm is employed for optimising the cost function of the GPC. The numerical results are presented on a two-area four-machine and a five-area 16-machine power system. The effectiveness of the designed controllers is shown by considering various operating conditions. The proposed approach, which is called as GPC-NHSFL, is compared with a classical-based method, GPC algorithm and GPC-based standard SFL algorithm (GPC-SFL). The simulation results show the superiority and capability of the proposed approach to enhance power systems damping.


    1. 1)
      • P. Kundur . (1994) Power system stability and control.
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
      • Bijami, E., Askari, J., Farsangi, M.M.: `Power system stabilizers design by using shuffled frog leaping', Sixth Int. Conf. on Technical and Physical Problems of Power Engineering, 14–16 September 2010, Tabriz, Iran, p. 342–346.
    12. 12)
      • Bijami, E., Jadidoleslam, M., Ebrahimi, A., Farsangi, M.M., Lee, K.Y.: `Power system stabilization using brain emotional learning based intelligent controller', IEEE Power Engineering Society General Meeting, 2011, USA.
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
      • E.F. Camacho , C. Bordons . (1995) Model predictive control in the process industry.
    18. 18)
      • J.M. Maciejowski . (2002) Predictive control with constraints.
    19. 19)
      • J.H. Chow . (1997) Power system toolbox: a set of coordinated m-files for use with MATLAB.
    20. 20)
    21. 21)
    22. 22)
    23. 23)
    24. 24)
    25. 25)
      • Huynh, T.H.: `A modified shuffled frog leaping algorithm for optimal tuning of multivariable PID controllers', IEEE Int. Conf. on Industrial Technology, 2008.
    26. 26)
      • Elbeltagi, E.: `Evolutionary algorithms for large scale optimization in construction management', The Future Trends in the Project Management, 2007, Riyadh, KSA.
    27. 27)
    28. 28)
      • Zhang, X., Hu, X., Gui, G., Wang, Y., Niu, Y.: `An improved shuffled frog leaping algorithm with cognitive behavior', Proc. Seventh World Congress on Intelligent Control and Automation, 2008, China.
    29. 29)
      • Li, Y., Zhou, J., Yang, J., Liu, L., Qin, H., Yang, L.: `The chaos-based shuffled frog leaping algorithm and its application', Fourth Int. Conf. on Natural Computation, 2008.
    30. 30)
      • Zhang, X., Hu, F., Tang, J., Zou, C., Zhao, L.: `A kind of composite shuffled frog leaping algorithm', Sixth Int. Conf. on Natural Computation, 2010.
    31. 31)
    32. 32)
      • Farsangi, M.M., Nezamabadi-Pour, H., Lee, K.Y.: `Multi-objective VAr planning with SVC for a large power system using PSO and GA', Proc. 2006 IEEE PES Power Systems Conf. and Exposition (PSCE), 29 October–1 November 2006, Atlanta, USA.
    33. 33)

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