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Robust Coordinated Design of Multiple Power System Stabilizers Using Particle Swarm Optimization Technique

Authors: Sidhartha Panda, C. Ardil


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 coordinately design multiple power system stabilizers (PSS) in a multi-machine power system. The design problem of the proposed controllers 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 for various severe disturbances and small disturbance 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.

Keywords: Low frequency oscillations, Particle swarm optimization, power system stability, power system stabilizer, multimachine power system.

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[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] Sidhartha Panda, N. P. Padhy, “Robust power system stabilizer design using particle swarm optimization technique”, International Journal of Electrical Systems Science and Engineering, Vol. 1, No. 1, pp. 1-8, 2008.
[7] PSO Tutorial,
[8] J. Kennedy, and R.C. Eberhart,. Particle swarm optimization. Proc. IEEE Int'l. Conf. on Neural Networks, IV, 1942-1948. Piscataway, NJ: IEEE Service Center. (1995) Available:
[9] M. Noroozian, G. Anderson, and K. Tomsovic, “Robust near-optimal control of power system oscillation with fuzzy logic, IEEE Trans. Power Delivery, vol. 11, no. 1, pp. 393–400, 1996.
[10] S. Mishra, P. K. Dash, P. K. Hota,.and M. Tripathy, “Genetically optimized neuro-fuzzy IPFC for damping modal oscillations of power systems”, IEEE Trans. Power Systems, vol. 17, no. 4, pp. 1140-1147, 2002.
[11] J Kennedy, R.C., Eberhart, Y. Shi. “Swarm Intelligence”. San Francisco: Morgan Kaufmann Publishers. 2001.
[12] M. Clarc., J. Kennedy, “The particle swarm – explosion, stability, and convergence in a multidimensional complex space”. IEEE Transactions on Evolutionary Computation. pp. 58-73. 2002.
[13] 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.
[14] SimPowerSystems 4.3 User’s Guide. Available:
[15] Birge, B. ” Particle Swarm Optimization Toolbox”. Available: