Genetically Optimized TCSC Controller for Transient Stability Improvement

Authors: Sidhartha Panda, N.P.Padhy, R.N.Patel

Abstract:

This paper presents a procedure for modeling and tuning the parameters of Thyristor Controlled Series Compensation (TCSC) controller in a multi-machine power system to improve transient stability. First a simple transfer function model of TCSC controller for stability improvement is developed and the parameters of the proposed controller are optimally tuned. Genetic algorithm (GA) is employed for the optimization of the parameter-constrained nonlinear optimization problem implemented in a simulation environment. By minimizing an objective function in which the oscillatory rotor angle deviations of the generators are involved, transient stability performance of the system is improved. The proposed TCSC controller is tested on a multi-machine system and the simulation results are presented. The nonlinear simulation results validate the effectiveness of proposed approach for transient stability improvement in a multimachine power system installed with a TCSC. The simulation results also show that the proposed TCSC controller is also effective in damping low frequency oscillations.

Keywords: Genetic algorithm, TCSC, transient stability, multimachinepower system.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1077074

References:

[1] N.G.Hingorani and L.Gyugyi, Understanding FACTS: Concepts and Technology of Flexible AC Transmission System. IEEE Press. 2000.
[2] Y.H.Song and T.A.Johns, Flexible AC Transmission Systems (FACTS). IEE. London. 2000.
[3] R.M Mathur and R. K.Verma, Thyristor-based FACTS Controllers for Electrical Transmission Systems. IEEE press. Piscataway. 2002.
[4] P.Mattavelli, G.C.Verghese and A. M. Stankovitctt, ''Phasor dynamics of Thyristor-Controlled series capacitor systems,'' IEEE Trans. Power Systs., vol-12, pp. 1259-1267, 1997.
[5] B. H Li, Q. H. Wu, D. R. Turner, P. Y. Wang and X.X Zhou, ''Modeling of TCSC dynamics for control and analysis of power system stability,'' Electrical Power & Energy Systs., Vol-22, pp. 43-49, 2000.
[6] L Fan, A. Feliachi and K. Schoder, ''Selection and design of A TCSC control signal in damping power system inter-area oscillations for multiple operating conditions.'' Electrical Power & Energy Systs., Vol-62, pp. 127-137, 2002.
[7] 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.
[8] S.Panda, R.N.Patel and N.P.Padhy, ''Power system stability improvement by TCSC controller employing a multi-objective genetic algorithm approach, International Journal of Intelligent Technology, Vol. 1, No. 4, pp. 266-273, 2006.
[9] D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, 1989.
[10] S.Mishra, P.K. Dash, P.K.Hota and M.Tripathy, ''Genetically optimized neuro-fuzzy IPFC for damping modal oscillations of power system.'' IEEE Trans. Power Systs., vol-17, pp. 1140-1147, 2002.
[11] T.K.Mok, H.Liu, Y.Ni, F.F.Wu and R.Hui, ''Tuning the fuzzy damping controller for UPFC through genetic algorithm with comparison to the gradient descent training,''Electric Power Systs. Research. Vol-27, pp. 275-283, 2005.
[12] M.A.Abido, ''Analysis and assessment of STATCOM-based damping stabilizers for power system stability enhancement.'' Electric Power Systs. Research. Vol-73, pp. 177-185, 2005.
[13] S. Panda and R.N.Patel, ''Optimal location of shunt FACTS controllers for transient stability improvement employing genetic algorithm'', Electric Power Components and Systems, Vol. 35, No. 2, pp. 189-203, 2007.
[14] Yong Chang and Zheng Xu, ''A novel SVC supplementary controller based on wide area signals'', Electric Power Syst. Research, Article in Press.
[15] P.W.Sauer and M.A.Pai, Power System Dynamics and Stability. Pearson education (Singapore) Pte. Ltd. 2002.
[16] H.Saadat, Power System Analysis. Tata McGraw-Hill, New Delhi, 2002.
[17] P. Kundur, Power System Stability and Control. New York: McGraw-Hill, 1994. 18) C.Houck, J.Joines and M.Kay, A genetic algorithm for function optimization: A MTLAM implementation. NCSU-IE, TR 95-09. 1995. Available: http://www.ise.ncsu.edu/mirage/GAToolBox/gaot
[18] C.Houck, J.Joines and M.Kay, A genetic algorithm for function optimization: A MTLAM implementation. NCSU-IE, TR 95–09. 1995. Available: http://www.ise.ncsu.edu/mirage/GAToolBox/gaot