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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1071818Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2247
 P. Kundur, Power System Stability and Control. New York: McGraw- Hill, 1994.
 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.
 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.
 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.
 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.
 PSO Tutorial, http://www.swarmintelligence.org/tutorials.php
 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
 Kennedy, J., Eberhart, R.C., Shi, Y. "Swarm Intelligence". San Francisco: Morgan Kaufmann Publishers. 2001.
 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.
 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.
 SimPowerSystems 4.3 User-s Guide. Available: http://www.mathworks.com/products/simpower/
 Birge, B. " Particle Swarm Optimization Toolbox". Available:http://www.mathworks.com/matlabcentral/fileexchange/