SciELO - Scientific Electronic Library Online

 
vol.11 número5Cell Assignment in Hybrid CMOS/Nanodevices Architecture Using a PSO/SA Hybrid AlgorithmTemperature and Thermal Stresses of Vehicles Gray Cast Brake índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados

Revista

Articulo

Indicadores

Links relacionados

  • No hay artículos similaresSimilares en SciELO

Compartir


Journal of applied research and technology

versión On-line ISSN 2448-6736versión impresa ISSN 1665-6423

J. appl. res. technol vol.11 no.5 Ciudad de México oct. 2013

 

A PSO Procedure for a Coordinated Tuning of Power System Stabilizers for Multiple Operating Conditions

 

Amin Safari

 

Department of Electrical Engineering, Ahar Branch Islamic Azad University, Ahar, Iran. a-safari@iau-ahar.ac.ir.

 

ABSTRACT

The problem of coordinated tuning stabilizers in multi-machine power systems is formulated here as a sequence of optimization problems. The design problem of stabilizers is converted to a nonlinear optimization problem with a multi-objective fitness function. The proposed method employs particle swarm optimization (PSO), an algorithm to search for optimal parameter settings of a widely used conventional fixed-structure lead-lag power system stabilizers (CPSSs). One of the main advantages of the proposed approach is its robustness to the initial parameter settings. In addition, the quality of the optimal solution does not rely on the initial guess. The robustness and performance of the newly designed controllers is evaluated in a large sixteen-machine power system subjected to different loading conditions in comparison with the genetic algorithm (GA) based PSSs design. The superiority of the controller designed is demonstrated through the nonlinear time-domain simulation and some performance indices studies. The results analysis reveals that the tuned PSSs with proposed objective function has an excellent capability in damping power system low-frequency oscillations.

Keywords: Power system stabilizer, multi-machine power system, PSO, dynamic stability.

 

DESCARGAR ARTÍCULO EN FORMATO PDF

 

References

[1] X. Hugang, Ch. Haozhong and L. Haiyu, Optimal reactive power flow incorporating static voltage stability based on multi-objective adaptive immune algorithm, Energy Conversion and Management., Vol. 49, pp. 1175-1181, 2008.         [ Links ]

[2] P. M. Anderson and A. A. Fouad, Power System Control and Stability, Ames, IA: Iowa State Univ. Press, 1977.         [ Links ]

[3] H. Shayeghi, H. A. Shayanfar, S. Jalilzadeh and A. Safari, Multi-machine power system stabilizers design using chaotic optimization algorithm, Energy Conversion and Management, Vol. 51, No. 7, pp. 1572-1580, 2010.         [ Links ]

[4] Y. L.Abdel-Magid, M. A. Abido, S. AI-Baiyat and A. H. Mantawy, Simultaneous stabilization of multimachine power systems via genetic algorithms, IEEE Transaction on Power System. Vol. 14, No. 4, pp. 1428-1439, 1999.         [ Links ]

[5] M. A. Abido and Y. L. Abdel-Magid, Hybridizing rule-based power system stabilizers with genetic algorithms, IEEE Transaction on Power System. Vol. 14, No. 2, pp. 600-607, 1999.         [ Links ]

[6] P. Zhang and A. H. Coonick, Coordinated synthesis of PSS parameters in multi-machine power systems using the method of inequalities applied to genetic algorithms, IEEE Transaction on Power System. Vol. 15, No. 2, pp. 811-816, 2000.         [ Links ]

[7] Y. L. Abdel-Magid, M. A. Abido and A. H. Mantawy, Robust tuning of power system stabilizers in multi-machine power systems. IEEE Transaction on Power System. Vol. 15, No. 2, pp. 735-740, 2000.         [ Links ]

[8] M. A. Abido, Robust design of multimachine power system stabilizers using simulated annealing, IEEE Transaction on Energy Conversion. Vol. 15, No. 3, pp. 297-304, 2003.         [ Links ]

[9] M. A. Abido and Y. L. Abdel-Magid, Optimal design of power system stabilizers using evolutionary programming, IEEE Transaction on Energy Conversion. Vol. 17, No. 4, pp. 429-436, 2002.         [ Links ]

[10] S. Mishra, M. Tripathy and J. Nanda, Multi-machine power system stabilizer design by rule based bacteria foraging, Electric Power System Research, Vol. 77, pp. 1595-1607, 2007.         [ Links ]

[11] K. Sebaa and M. Boudourb, Optimal locations and tuning of robust power system stabilizer using genetic algorithms, Electric Power Systems Research, Vol. 79, pp. 406-416, 2009.         [ Links ]

[12] H. Rezazadeh, M. Ghazanfari, S. J. Sadjadi, M. B. Aryanezhad and A. Makui, Linear programming embedded particle swarm optimization for solving an extended model of dynamic virtual cellular manufacturing systems, Journal of Applied Research and Technology, Vol. 7, No. 1, pp. 83-108, 2009.         [ Links ]

[13] G. A. Laguna-Sanchez, M. Olguin-Carbajal, N. Cruz-Cortes, R. Barron-Fernandez and J. A. Alvarez-Cedillo, Comparative Study of Parallel Variants for a Particle Swarm Optimization, Journal of Applied Research and Technology, Vol. 7, No. 3, pp. 292-309, 2009.         [ Links ]

[14] H. Shayeghi, H. A. Shayanfar, S. Jalilzadeh and A. Safari, A PSO based unified power flow controller for dampingof power system oscillations, Energy Conversion and Management, Vol. 50, pp. 2583-2592, 2009.         [ Links ]

[15] D. Rosas, C. Amaro, & Alvarez J., Control of a saddle node bifurcation in a power system using a PID controller, Journal of Applied Research and Technology, Vol. 1, No. 1, pp. 94-102, 2003.         [ Links ]

[16] G. Rogers, Power System Oscillations, Kluwer Academic Publishers, 2002.         [ Links ]

Creative Commons License Todo el contenido de esta revista, excepto dónde está identificado, está bajo una Licencia Creative Commons