Robust FACTS Controller Design Employing Modern Heuristic Optimization Techniques
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.
[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.
[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.
@article{"International Journal of Electrical, Electronic and Communication Sciences:51286", author = "A.K.Balirsingh and S.C.Swain and S. Panda", title = "Robust FACTS Controller Design Employing Modern Heuristic Optimization Techniques", 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.", volume = "5", number = "8", pages = "935-10", }