Robust FACTS Controller Design Employing Modern Heuristic Optimization Techniques

Authors: A.K.Balirsingh, S.C.Swain, S. Panda

Abstract:

Recently, Genetic Algorithms (GA) and Differential Evolution (DE) algorithm 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 DE and GA optimization techniques, for flexible ac transmission system (FACTS)-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 has been compared. Further, the optimized controllers are tested on a weekly 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 FACTS-based controller, to enhance power system stability.

Keywords: Differential Evolution, Flexible AC TransmissionSystems (FACTS), Genetic Algorithm, Low Frequency Oscillations, Single-machine Infinite Bus Power System.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1056561

References:

[1] N. G. Hingorani and L. Gyugyi, Understanding FACTS: Concepts and Technology of Flexible AC Transmission Systems, IEEE Press, New York, 2000.
[2] Y. H. Song, Flexible AC Transmission Systems (FACTS). The Institution of Electrical Engineers, London, 1999.
[3] R. M. Mathur and R. K. Varma, Thyristor-based FACTS Controllers for Electrical Transmission Systems, IEEE Press, Piscataway, 2002.
[4] P. Kundur, Power System Stability and Control, McGraw-Hill, 1994.
[5] Sidhartha Panda, N.P. Padhy, "Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design", Applied Soft Computing, Vol. 8, Issue 4, pp. 1418- 1427, 2008.
[6] Sidhartha Panda, S.C. Swain, P.K. Rautray, R. Mallik, G. Panda, "Design and analysis of SSSC-based supplementary damping controller", Simulation Modelling Practice and Theory, Vol. 18, pp. 1199-1213, 2010.
[7] S.C. Swain, A.K. Baliarsingh, S.Mahapatra, S. Panda, "Design of Static Synchronous Series Compensator Based Damping Controller Employing Real Coded Genetic Algorithm", International Journal of Electrical and Electronics Engineering, Vol. 5, No. 3, pp. 180-188, 2011.
[8] Sidhartha Panda, N.P. Padhy, R.N. Patel, "Application and comparison of intelligent optimisation techniques for SSSCbased controller design", Int. J. Intelligent System Technology and Applications. Vol. 9, No. 2, pp. 169-184, 2010.
[9] Sidhartha Panda, "Multi-objective evolutionary algorithm for SSSC-based controller design", Electric Power System Research, Vol. 79, Issue 6, pp. 937-944, 2009.
[10] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.
[11] R. Stron, K. Price, Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces, J. Global Optim. 11 (1997) 341-359.
[12] Sidhartha Panda, "Differential evolution algorithm for SSSCbased damping controller design considering time delay", Article in press in Journal of the Franklin Institute, DOI: 10.1016/j.jfranklin.2011.05.011.
[13] Sidhartha Panda, "Robust coordinated design of multiple and multi-type damping controller using differential evolution algorithm", International Journal of Electrical Power and Energy Systems, Vol. 33, 1018-1030, 2011.