Tuning of Power System Stabilizers in a Multi- Machine Power System using C-Catfish PSO

The main objective of this paper is to investigate the enhancement of power system stability via coordinated tuning of Power System Stabilizers (PSSs) in a multi-machine power system. The design problem of the proposed controllers is formulated as an optimization problem. Chaotic catfish particle swarm optimization (C-Catfish PSO) algorithm is used to minimize the ITAE objective function. The proposed algorithm is evaluated on a two-area, 4- machines system. The robustness of the proposed algorithm is verified on this system under different operating conditions and applying a three-phase fault. The nonlinear time-domain simulation results and some performance indices show the effectiveness of the proposed controller in damping power system oscillations and this novel optimization algorithm is compared with particle swarm optimization (PSO).




References:
[1] Basler MJ, Schaefer RC. Understanding power system stability. IEEE
Trans Indus Appl 2008;44:463-74.
[2] Andreoiu A. On power systems stabilizers: genetic algorithm based
tuning and economic worth as ancillary services. Ph.D. Thesis.
Chalmers University; 2004.
[3] Xia D, Heydt GT. Self-tuning controller for generator excitation
control. IEEE trans PAS 1983;102:1877-85.
[4] Cao Y, jiang L, Cheng S, Chen D, Malik OP, Hope GS. A nonlinear
variable structure stabilizer for power system stability. IEEE Trans EC
1994;9(3):489-95.
[5] Abido MA, Abdel-Majid YL. A hybrid Neuro-fuzzy power system
stabilizer for multimachine power systems. IEEE Trans PWRS
1998;13(4):1323-30.
[6] Fraile-Ardanuy J, Zufiria PJ. Design and comparison of adaptive power
system stabilizers based on neural fuzzy networks and genetic
algorithms. Neurocomputing 2007;70:2902-12
[7] Segal R, Sharma A, Kothari ML. A self-tuning power system stabilizer
based on artificial neural network. Elect Power Energy Syst
2004;26:423-30.
[8] Hardiansyah FS, Furuya S, Irisawa J. A robust H ¥
power system
stabilizer design using reduced-order models. Elect Power Energy Syst
2006;28:21-8.
[9] Abdel-Magid YL, Abido MA, AI-Baiyat S, Mantawy AH. Simultaneous
stabilization of multimachine power systems via genetic algorithms.
IEEE Trans Power Syst 1999;14(4):1428-39.
[10] Abido MA, Abdel-Magid YL. Hybridizing rule-based power system
stabilizers with genetic algorithms. IEEE Trans Power Syst
1999;14(2):600-7.
[11] Zhang P, Coonick AH. Coordinated synthesis of PSS parameters in
multimachine power systems using the method of inequalities applied to
genetic algorithms. IEEE Trans Power Syst 2000;15(2):811-6.
[12] Abdel-Magid YL, Abido MA. Optimal multiobjective design of robust
power system stabilizers using genetic algorithms. IEEE Trans Power
Syst 2003;18(3):1125-32.
[13] Abdel-Magid YL, Abido MA, Mantawy AH. Robust tuning of power
system stabilizers in multimachine power systems. IEEE Trans Power
Syst 2000;15(2):735-40.
[14] Abido MA. Robust design of multimachine power system stabilizers
using simulated annealing. IEEE Trans Energy Convers
2003;15(3):297-304.
[15] Abido MA, Abdel-Magid YL. Optimal design of power system
stabilizers using evolutionary programming. IEEE Trans Energy
Convers 2002;17(4):429-36.
[16] Mishra S, Tripathy M, Nanda J. Multi-machine power system stabilizer
design by rule based bacteria foraging. Electr Power Syst Res
2007;77:1595-607.
[17] Ricardo V. de Oliveira, Rodrigo A. Ramos, Newton G. Bretas. An
algorithm for computerized automatic tuning of power system
stabilizers. Control Engineering Practice 18 (2010) 45-54
[18] H. Shayeghi, H.A. Shayanfar, A. Safari, R. Aghmasheh. A robust PSSs
design using PSO in a multi-machine environment. Energy Conversion
and Management 51 (2010) 696-702
[19] Fukuyama, Y. (1999). A particle swarm optimization for reactive power
and voltage control considering voltage stability. Proceedings of IEEE
international conference on intelligent system applications to power
systems (ISAP), Rio de Janeiro.
[20] Shayeghi H, Jalili A, Shayanfar HA. Multi-stage fuzzy load frequency
control using PSO. Energy Convers Manage 2008;49:2570-80.
[21] Kennedy J, Eberhart R, Shi Y. Swarm intelligence. San Francisco:
Morgan Kaufmann Publishers; 2001.
[22] dos Santos Coelho, V.C. Mariani, A novel chaotic particle swarm
optimization approach using Hénon map and implicit filtering local
search for economic load dispatch, Chaos, Solitons and Fractals 39
(2009) 510-518.
[23] Y. Shi and R.C. Eberhart, Empirical study of particle swarm
optimization, in: Proceedings of Congress on Evolutionary
Computation, Washington, DC, 2002, pp. 1945-1949.
[24] H. Gao, Y. Zhang, S. Liang, D. Li, A new chaotic algorithm for image
encryption, Chaos, Solitons and Fractals 29 (2006) 393-399.
[25] D. Kuo, Chaos and its computing paradigm, IEEE Potentials Magazine
24 (2005) 13-15.
[26] Li-Yeh Chuang, Sheng-Wei Tsai, Cheng-Hong Yang, Chaotic catfish
particle swarm optimization for solving global numerical optimization
problems, Applied Mathematics and Computation 217 (2011) 6900-
6916
[27] Kundur P. Power system stability and control. New York: McGraw Hill;
1994.
[28] Sadikovic R. Damping controller design for power system oscillations.
Zurich: Internal Report; 2004.
[29] Mishra S. Neural network based adaptive UPFC for improving transient
stability performance of power system. IEEE Trans Neural Networks
2006;17(2):461-70.
[30] Nguyen TT, Gianto R. Optimisation based control coordination of PSSs
and FACTS devices for optimal oscillations damping in multi-machine
power system. IEE Proc Gener Transm Distrib 2007;1(4):564-73.
[31] Shayeghi H, Shayanfar HA, Jalili A. Multi stage fuzzy PID power
system automatic generation controller in deregulated environments.
Energy Convers Manage 2006;47:2829-45.