APPLICATION AND COMPARATIVE PERFORMANCE ANALYSIS OF PSO AND ABC ALGORITHMS FOR OPTIMAL DESIGN OF MULTI-MACHINE POWER SYSTEM STABILIZERS

Abstract

This paper presents the application and performance comparison of PSO and ABC optimization techniques, for multi-objective design of power system stabilizers (PSSs) in the multi-machine power system. The design objective is to improve the power system stability. The PSSs parameters tuning problem is converted to an optimization problem with the time domain-based objective function and both PSO and ABC optimization techniques are used to search for optimal stabilizers parameters. The optimized stabilizers are tested on multi-machine electric power system subjected to different disturbances. The performance of both optimization techniques in terms of computational time, convergence rate and solution quality is compared. The eigenvalue analysis, nonlinear time-domain simulation results, critical clearing times and some performance indices studies are introduced and compared in order to demonstrate the effectiveness of both optimization techniques in designing stabilizers, to enhance the dynamic stability of the system. What is more, the potential and superiority of the ABC algorithm over the PSO algorithm are verified.

Keywords

• Artificial Bee Colony (ABC) algorithm,Particle Swarm Optimization (PSO); PSS design; dynamic stability; multi-machine power system.

References

1. Fereidouni AR, Vahidi B, Hoseini Mehr T, Tahmasbi M. Improvement of low frequency oscillation damping by allocation and design of power system stabilizers in the multi-machine power system. Int J of Electr Power Energy Syst 2013; 52:207-220.
2. Shayeghi H, Shayanfar HA, Safari A, Aghmasheh R. A robust PSSs design using PSO in a multi- machine environment. Energy Convers Manage 2010; 51(4):696-702.
3. Abido MA. Optimal design of power-system stabilizers using particle swarm optimization. IEEE Trans on Energy Convers 2002; 17(3):406- 413.
4. Kennedy J, Eberhart RC, Shi Y. Swarm intelligence. San Francisco: Morgan Kaufmann, 2001.
5. Karaboga D, Akay B. A comparative study of Colony Artificial Mathematics and Computation 2009; 214(1):108- 132. algorithm. Applied
6. Sauer PW, Pai MA. Power system Dynamics and Stability. Prentice Hall, 1998.
7. Kundur P. Power System Stability and Control. New York, USA: McGraw-Hill, 1994.
8. Keumarsi V, Simab M, Shahgholian G. An integrated approach for optimal placement and tuning of power system stabilizer in multi-machine systems. Int J of Electr Power Energy Syst 2014; 63:132-139.

9. Karaboga D, Basturk B. On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing 2008; 8(1):687-697.
10. Karaboga D, Ozturk C. A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Applied Soft Computing 2011; 11(1):652-657.
11. Eke İ, Taplamacıoğlu MC and Kocaarslan İ. Design of robust power system stabilizer based on Artificial Journal of The Faculty of Engineering and Architecture 2011; 26(3):683-690. Algorithm. of Gazi University
12. Pai MA. Energy function analysis for power USA: system publishers, 1989. Kluwer Academic
13. Padiyar KR. Power System Dynamics: Stability and Control. Hyderabad, India: B.S. Publications, 2002.
14. Hsu YY, Chen, CL. Identification of Optimum Location for Stabilizer Applications Using Participation Factors. IEE Proceedings C Gen Trans Distr 1987; 134(3):238-244.
15. Zhou N; Wang P, Wang Q, Loh PC. Transient Stability Study of Distributed Induction Generators Using an Improved Steady-State Equivalent Circuit Method. IEEE Trans on Power Syst 2014; 29(2):608-616.
16. Dorf RC, Bishop RH. Modern Control Systems. Prentice Hall, 2010.