Multi-objective Optimization with Fuzzy Based Ranking for TCSC Supplementary Controller to Improve Rotor Angle and Voltage Stability
Many real-world optimization problems involve multiple conflicting objectives and the use of evolutionary algorithms to solve the problems has attracted much attention recently. This paper investigates the application of multi-objective optimization technique for the design of a Thyristor Controlled Series Compensator (TCSC)-based controller to enhance the performance of a power system. The design objective is to improve both rotor angle stability and system voltage profile. A Genetic Algorithm (GA) based solution technique is applied to generate a Pareto set of global optimal solutions to the given multi-objective optimisation problem. Further, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto solution set. Simulation results are presented to show the effectiveness and robustness of the proposed approach.
[1] P. Kundur, Power System Stability and Control, McGraw-Hill, 1994
[2] A. D Del Rosso, C. A Canizares and V.M. Dona, "A study of TCSC
controller design for power system stability improvement," IEEE Trans.
Power Systs., vol-18, pp. 1487-1496. 2003.
[3] S. Panda, N. P. Padhy, R. N. Patel "Modeling, simulation and optimal
tuning of TCSC controller", International Journal ofSimulation
Modelling. Vol. 6, No. 1, pp. 7-48, 2007.
[4] S. Panda, and N. P. Padhy "Comparison of Particle Swarm Optimization
and Genetic Algorithm for FACTS-based Controller Design", Applied
Soft Computing. Vol. 8, pp. 1418-1427, 2008.
[5] S. Panda, S. C. Swain, A. K. Baliarsingh, C. Ardil, "Optimal
Supplementary Damping Controller Design for TCSC Employing
RCGA", International Journal of Computational Intelligence, Vol. 5,
No. 1, pp. 36-45, 2009.
[6] Sidhartha Panda and Narayana Prasad Padhy, "Application of Genetic
Algorithm for PSS and FACTS based Controller Design", International
Journal of Computational Methods, Vol. 5, Issue 4, pp. 607-620, 2008.
[7] S. Panda and R.N.Patel, "Damping Power System Oscillations by
Genetically Optimized PSS and TCSC Controller" International Journal
of Energy Technology and Policy, Vol. 5, No. 4, pp. 457-474, 2007.
[8] S. Panda, N.P.Padhy and R.N.Patel, "Robust Coordinated Design of PSS
and TCSC using PSO Technique for Power System Stability
Enhancement", Journal of Electrical Systems, Vol. 3, No. 2, pp. 109-
123, 2007.
[9] Sidhartha Panda, N.P.Padhy, "Thyristor Controlled Series Compensatorbased
Controller Design Employing Genetic algorithm: A Comparative
Study" International Journal of Electronics, Circuits and Systems, Vol.
1, No. 1, pp. 38-47, 2007.
[10] C. A .C. Coello, "A comprehensive survey of evolutionary-based
multiobjective optimization techniques", Knowledge and Information
Systems, Vol. 1, No. 3, pp. 269-308. , 1999.
[11] V. Chankong and Y. Haimes, Multiobjective Decision Making Theory
and Methodology, New York: North-Holland. 1983.
[12] C. M Fonseca, and P. J. Fleming, ÔÇÿGenetic algorithms for multiobjective
optimization: formulation, discussion and generalization, Proceedings of
the Fifth International Conference on Genetic Algorithms, San Mateo
California. pp. 416-423, 1993.
[13] 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.
[14] S. S. Rao, Optimization Theory and Application,New Delhi: Wiley
Eastern Limited, 1991.
[15] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and
Machine Learning, Addison-Wesley, 1989.
[16] Sidhartha Panda "Multi-objective evolutionary algorithm for SSSCbased
controller design", Electric Power System Research., Vol. 79,
Issue 6, pp. 937-944, 2009.
[1] P. Kundur, Power System Stability and Control, McGraw-Hill, 1994
[2] A. D Del Rosso, C. A Canizares and V.M. Dona, "A study of TCSC
controller design for power system stability improvement," IEEE Trans.
Power Systs., vol-18, pp. 1487-1496. 2003.
[3] S. Panda, N. P. Padhy, R. N. Patel "Modeling, simulation and optimal
tuning of TCSC controller", International Journal ofSimulation
Modelling. Vol. 6, No. 1, pp. 7-48, 2007.
[4] S. Panda, and N. P. Padhy "Comparison of Particle Swarm Optimization
and Genetic Algorithm for FACTS-based Controller Design", Applied
Soft Computing. Vol. 8, pp. 1418-1427, 2008.
[5] S. Panda, S. C. Swain, A. K. Baliarsingh, C. Ardil, "Optimal
Supplementary Damping Controller Design for TCSC Employing
RCGA", International Journal of Computational Intelligence, Vol. 5,
No. 1, pp. 36-45, 2009.
[6] Sidhartha Panda and Narayana Prasad Padhy, "Application of Genetic
Algorithm for PSS and FACTS based Controller Design", International
Journal of Computational Methods, Vol. 5, Issue 4, pp. 607-620, 2008.
[7] S. Panda and R.N.Patel, "Damping Power System Oscillations by
Genetically Optimized PSS and TCSC Controller" International Journal
of Energy Technology and Policy, Vol. 5, No. 4, pp. 457-474, 2007.
[8] S. Panda, N.P.Padhy and R.N.Patel, "Robust Coordinated Design of PSS
and TCSC using PSO Technique for Power System Stability
Enhancement", Journal of Electrical Systems, Vol. 3, No. 2, pp. 109-
123, 2007.
[9] Sidhartha Panda, N.P.Padhy, "Thyristor Controlled Series Compensatorbased
Controller Design Employing Genetic algorithm: A Comparative
Study" International Journal of Electronics, Circuits and Systems, Vol.
1, No. 1, pp. 38-47, 2007.
[10] C. A .C. Coello, "A comprehensive survey of evolutionary-based
multiobjective optimization techniques", Knowledge and Information
Systems, Vol. 1, No. 3, pp. 269-308. , 1999.
[11] V. Chankong and Y. Haimes, Multiobjective Decision Making Theory
and Methodology, New York: North-Holland. 1983.
[12] C. M Fonseca, and P. J. Fleming, ÔÇÿGenetic algorithms for multiobjective
optimization: formulation, discussion and generalization, Proceedings of
the Fifth International Conference on Genetic Algorithms, San Mateo
California. pp. 416-423, 1993.
[13] 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.
[14] S. S. Rao, Optimization Theory and Application,New Delhi: Wiley
Eastern Limited, 1991.
[15] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and
Machine Learning, Addison-Wesley, 1989.
[16] Sidhartha Panda "Multi-objective evolutionary algorithm for SSSCbased
controller design", Electric Power System Research., Vol. 79,
Issue 6, pp. 937-944, 2009.
@article{"International Journal of Electrical, Electronic and Communication Sciences:59315", author = "S. Panda and S. C. Swain and A. K. Baliarsingh and A. K. Mohanty and C. Ardil", title = "Multi-objective Optimization with Fuzzy Based Ranking for TCSC Supplementary Controller to Improve Rotor Angle and Voltage Stability", abstract = "Many real-world optimization problems involve multiple conflicting objectives and the use of evolutionary algorithms to solve the problems has attracted much attention recently. This paper investigates the application of multi-objective optimization technique for the design of a Thyristor Controlled Series Compensator (TCSC)-based controller to enhance the performance of a power system. The design objective is to improve both rotor angle stability and system voltage profile. A Genetic Algorithm (GA) based solution technique is applied to generate a Pareto set of global optimal solutions to the given multi-objective optimisation problem. Further, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto solution set. Simulation results are presented to show the effectiveness and robustness of the proposed approach.
", keywords = "Multi-objective optimisation, thyristor controlled series compensator, power system stability, genetic algorithm, pareto solution set, fuzzy ranking.", volume = "6", number = "3", pages = "335-8", }
