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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1074889Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1773
 P. Kundur, Power System Stability and Control, McGraw-Hill, 1994
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 V. Chankong and Y. Haimes, Multiobjective Decision Making Theory and Methodology, New York: North-Holland. 1983.
 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.
 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.
 S. S. Rao, Optimization Theory and Application,New Delhi: Wiley Eastern Limited, 1991.
 D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.
 Sidhartha Panda "Multi-objective evolutionary algorithm for SSSCbased controller design", Electric Power System Research., Vol. 79, Issue 6, pp. 937-944, 2009.