Comparison of Particle Swarm Optimization and Genetic Algorithm for TCSC-based Controller Design

Recently, genetic algorithms (GA) and particle swarm optimization (PSO) technique have attracted considerable attention among various modern heuristic optimization techniques. Since the two approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their performance. This paper presents the application and performance comparison of PSO and GA optimization techniques, for Thyristor Controlled Series Compensator (TCSC)-based controller design. The design objective is to enhance the power system stability. The design problem of the FACTS-based controller is formulated as an optimization problem and both the PSO and GA optimization techniques are employed to search for optimal controller parameters. The performance of both optimization techniques in terms of computational time and convergence rate is compared. Further, the optimized controllers are tested on a weakly connected power system subjected to different disturbances, and their performance is compared with the conventional power system stabilizer (CPSS). The eigenvalue analysis and non-linear simulation results are presented and compared to show the effectiveness of both the techniques in designing a TCSC-based controller, to enhance power system stability.

[1] D.E. Goldberg, Genetic Algorithms in Search, Optimization, and
Machine Learning, Addison-Wesley, 1989.
[2] Y. L. Abdel-Magid, M. A. Abido, Coordinated Design of a PSS and a
SVC-based Controller to Enhance Power System Stability, Int. J. of
Electrical Power & Energy Systems, Vol. 25, pp. 695-704, 2003.
[3] A. Varsek, T. Urbancic, B. Filipic, Genetic Algorithm in Controller
Design and Tuning, IEEE Trans. System, Man, and Cybernetics, Vol. 23,
No. 5, pp. 1330-1339, 1993.
[4] J. M. Ramirez, I. Castillo, PSS and FDS Simultaneous Tuning, Electrical
Power System Research, Vo. 68, pp. 33-40, 2004.
[5] M. A. Abido, Analysis and Assessment of STATCOM-based Damping
Stabilizer for Power System Stability Enhancement, Electrical Power
System Research, Vol. 73, pp. 177-185, 2005.
[6] J. Kennedy, R. C. Eberhart, Particle Swarm Optimization, Proc. IEEE
Int'l. Conf. on Neural Networks, IV, 1942-1948. Piscataway, NJ, IEEE
Service Center, 1995.
[7] PSO Tutorial,
[8] N. G. Hingorani, L.Gyugyi, Understanding FACTS: concepts and
technology of flexible AC transmission systems, IEEE Press, New York,
[9] P. Mattavelli, G. C. Verghese, A.M. Stankovitctt, Phasor Dynamics of
Thyristor-Controlled Series Capacitor Systems, IEEE Trans. Power
Syst. Vol. 12, No. 3, pp. 1259-1267, 1997.
[10] B. H. Li, Q. H Wu, D. R. Turner, P. Y. Wang, X. X Zhou, Modeling of
TCSC Dynamics for Control and Analysis of Power System Stability,
Int. J. of Electrical Power & Energy Systems, Vol. 22, No. 1, pp. 43-49,
[11] A. D. Del Rosso, C. A Canizares, V.M. Dona, A study of TCSC
Controller Design for Power System Stability Improvement, IEEE
Trans. Power Syst., Vol. 18, No. 4, pp. 1487-1496, 2003.
[12] Yao-Nan Yu, Power system dynamics, Academic press, Inc., London,
[13] Y. L. Abdel-Magid, M. A. Abido, Robust Coordinated Design of
Excitation and TCSC-based Stabilizers using Genetic Algorithms, Int. J.
of Electrical Power & Energy Systems, Vol. 69, No. 2-3, pp. 29-141,
[14] H. F. Wang, F. J. Swift, A Unified Model of FACTS Devices in
Damping Power System Oscillations Part-1: Single-machine Infinite-bus
Power Systems, IEEE Trans. Power Delivery, Vol. 12, No. 2, pp. 941-
946, 1997.
[15] S. Panda, R. N. Patel, N. P. Padhy, Power System Stability Improvement
by TCSC Controller Employing a Multi-objective Genetic Algorithm
Approach, Int. J. of Intelligent Technology, Vol. 1, No. 4, pp. 266-273,
[16] J. Kennedy, R. Eberhart, Swarm Intelligence, Academic press, 1 st ed.
San Diego, CA, 2001.
[17] Z.L. Gaing, 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, 2004.
[18] P. Kundur, Power System Stability and Control, McGraw-Hill, 1994.
[19] B. Birge, Particle Swarm Optimization Toolbox,
[20] C. Houck, J. Joines, M. Kay, A Genetic Algorithm for Function
Optimization: A Matlab Implementation-, NCSU-IE TR 95-05. 1995.