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

The problem of coordinated tuning stabilizers in multi-machine power systems is formulated here as a sequence ofoptimization problems. The design problem of stabilizers is converted to a nonlinear optimization problem with a multiobjectivefitness function. The proposed method employs particle swarm optimization (PSO), an algorithm to searchfor 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. Inaddition, the quality of the optimal solution does not rely on the initial guess. The robustness and performance of thenewly designed controllers is evaluated in a large sixteen-machine power system subjected to different loadingconditions in comparison with the genetic algorithm (GA) based PSSs design. The superiority of the controllerdesigned is demonstrated through the nonlinear time-domain simulation and some performance indices studies. Theresults analysis reveals that the tuned PSSs with proposed objective function has an excellent capability in dampingpower system low-frequency oscillations.

[1]  M. A. Abido,et al.  Hybridizing rule-based power system stabilizers with genetic algorithms , 1999 .

[2]  Ahmad Makui,et al.  Linear programming embedded particle swarm optimization for solving an extended model of dynamic virtual cellular manufacturing systems , 2009 .

[3]  A. H. Coonick,et al.  Coordinated synthesis of PSS parameters in multi-machine power systems using the method of inequalities applied to genetic algorithms , 2000 .

[4]  Amin Safari,et al.  A PSO based unified power flow controller for damping of power system oscillations , 2009 .

[5]  K. Sebaa,et al.  Optimal Locations and tuning of Robust Power System Stabilizers using Genetic Algorithms , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

[6]  J. Alvarez,et al.  CONTROL OF A SADDLE NODE BIFURCATION IN A POWER SYSTEM USING A PID CONTROLLER , 2003 .

[7]  Laguna,et al.  Comparative Study of Parallel Variants for a Particle Swarm Optimization Algorithm Implemented on a Multithreading Gpu , 2009 .

[8]  Mohammad Ali Abido,et al.  Robust tuning of power system stabilizers in multimachine power systems , 2000 .

[9]  Haozhong Cheng,et al.  Optimal reactive power flow incorporating static voltage stability based on multi-objective adaptive immune algorithm , 2008 .

[10]  Graham Rogers,et al.  Power System Oscillations , 1999 .

[11]  H. Happ Power system control and stability , 1979, Proceedings of the IEEE.

[12]  J. Nanda,et al.  Multi-machine power system stabilizer design by rule based bacteria foraging , 2007 .

[13]  Amin Safari,et al.  Multi-machine power system stabilizers design using chaotic optimization algorithm , 2010 .

[14]  M. A. Abido,et al.  Robust design of multimachine power system stabilizers using simulated annealing , 2000 .

[15]  M. A. Abido,et al.  Optimal Design of Power System Stabilizers Using Evolutionary Programming , 2002, IEEE Power Engineering Review.

[16]  M. A. Abido,et al.  Simultaneous stabilization of multimachine power systems via genetic algorithms , 1999, IEEE Transactions on Power Systems.