Robust Power System Stabilizer Design Using Particle Swarm Optimization Technique

Power system stabilizers (PSS) are now routinely used in the industry to damp out power system oscillations. In this paper, particle swarm optimization (PSO) technique is applied to design a robust power system stabilizer (PSS). The design problem of the proposed controller is formulated as an optimization problem and PSO is employed to search for optimal controller parameters. By minimizing the time-domain based objective function, in which the deviation in the oscillatory rotor speed of the generator is involved; stability performance of the system is improved. The non-linear simulation results are presented under wide range of operating conditions; disturbances at different locations as well as for various fault clearing sequences to show the effectiveness and robustness of the proposed controller and their ability to provide efficient damping of low frequency oscillations. Further, all the simulations results are compared with a conventionally designed power system stabilizer to show the superiority of the proposed design approach.

• Sidhartha Panda
• N. P. Padhy

• Particle swarm optimization
• power system stabilizer
• low frequency oscillations
• power system stability.

[1] P. Kundur, Power System Stability and Control. New York: McGraw-
Hill, 1994.
[2] P. Kundur, M. Klein, G. J. Rogers, and M. S. Zywno, "Application of
power system stabilizers for enhancement of overall system stability,"
IEEE Trans. Power Syst., vol. 4, pp. 614-626, 1989.
[3] M. A. Abido , "Pole placement technique for PSS and TCSC-based
stabilizer design using simulated annealing" Electrical Power and
Energy Systems ,vol-22 , pp 543-554, 2000.
[4] Y.L. Abdel-Magid, M.A. Abido, "Coordinated design of a PSS and a
SVC-based controller to enhance power system stability. Electrical
Power & Energy Syst, vol. 25, pp. 695-704, 2003.
[5] Y.L. Abdel-Magid and M.A.Abido, "Robust coordinated design of
excitation and TCSC-based stabilizers using genetic algorithms,
International Journal of Electrical Power & Energy Systems, vol. 69, no.
2-3, pp. 129-141. 2004.
[6] PSO Tutorial, http://www.swarmintelligence.org/tutorials.php
[7] Kennedy, J. and Eberhart, R.C. (1995). Particle swarm optimization.
Proc. IEEE Int'l. Conf. on Neural Networks, IV, 1942-1948. Piscataway,
NJ: IEEE Service Center.
Available: http://www.engr.iupui.edu/~shi/Coference/psopap4.html
[8] Kennedy, J., Eberhart, R.C., Shi, Y. "Swarm Intelligence". San
Francisco: Morgan Kaufmann Publishers. 2001.
[9] Clarc, M., Kennedy, J. "The particle swarm - explosion, stability, and
convergence in a multidimensional complex space". IEEE Transactions
on Evolutionary Computation. pp. 58-73. 2002.
[10] Gaing, Z.L. "A particle swarm optimization approach for optimum
design of PID controller in AVR system". IEEE Trans. Energy Conv.
Vol. 9, No. 2, pp. 384-391, June2004.
[11] SimPowerSystems 4.3 User-s Guide. Available:
http://www.mathworks.com/products/simpower/
[12] Birge, B. " Particle Swarm Optimization Toolbox".
Available:http://www.mathworks.com/matlabcentral/fileexchange/